{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f8d11457-a07c-48a6-9946-424e90980b60",
   "metadata": {},
   "source": [
    "# Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f64e8fa7-3a31-4cde-aba9-0937d79358bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T22:35:26.856556Z",
     "iopub.status.busy": "2024-03-28T22:35:26.856556Z",
     "iopub.status.idle": "2024-03-28T22:35:32.982380Z",
     "shell.execute_reply": "2024-03-28T22:35:32.981378Z",
     "shell.execute_reply.started": "2024-03-28T22:35:26.856556Z"
    },
    "scene__Initialisation": true,
    "tags": [
     "ActiveScene"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing instrument manager...OK\n",
      "Weights loaded\n"
     ]
    }
   ],
   "source": [
    "import importlib\n",
    "%matplotlib inline\n",
    "import matplotlib.gridspec as gridspec\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes\n",
    "from mpl_toolkits.axes_grid1.inset_locator import mark_inset\n",
    "import time as time\n",
    "import scipy.optimize as sp\n",
    "import scipy\n",
    "import shutil\n",
    "import pandas as pd\n",
    "from resonator import background, see, reflection\n",
    "import lmfit\n",
    "from sympy import divisors\n",
    "import importlib.util\n",
    "import sys\n",
    "from tqdm.notebook import tqdm\n",
    "    \n",
    "from qutip import *\n",
    "from scipy.sparse import dia_matrix\n",
    "from scipy.linalg import sqrtm, expm\n",
    "\n",
    "from Config import *\n",
    "from PlottingLibrary import *\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "import h5py\n",
    "\n",
    "from scipy.stats import norm\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy import signal\n",
    "from scipy.signal import find_peaks\n",
    "import scipy.constants as cst\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "import cvxpy as cp\n",
    "\n",
    "################################################\n",
    "import seaborn as sns\n",
    "plt.rcParams['axes.formatter.useoffset'] = False\n",
    "sns.set(font_scale=1.5, style='ticks')\n",
    "sns.set_style({\"xtick.direction\": \"in\",\"ytick.direction\": \"in\",'ytick.right': True,'xtick.top': True})\n",
    "cols = [\"#387FA7\",\"darkorange\",\"seagreen\",\"indianred\",\"purple\"]\n",
    "sns.set_palette(cols)\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']*10\n",
    "\n",
    "################################################\n",
    "\n",
    "import pickle\n",
    "\n",
    "kmeans_standard = pickle.load(open(f'Z:\\SMPD3-8\\SpinRun3-1\\standard_kmean.model', 'rb'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "badb4f2f-6312-499c-a195-7a20efc4092f",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "afab21f7-134d-49ec-b472-e4d07f93a667",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T23:10:56.762294Z",
     "iopub.status.busy": "2024-03-28T23:10:56.762294Z",
     "iopub.status.idle": "2024-03-28T23:10:56.898294Z",
     "shell.execute_reply": "2024-03-28T23:10:56.897294Z",
     "shell.execute_reply.started": "2024-03-28T23:10:56.762294Z"
    },
    "scene__Initialisation": true,
    "tags": [
     "ActiveScene"
    ]
   },
   "outputs": [],
   "source": [
    "def PrintStatic(s):\n",
    "    sys.stdout.flush()\n",
    "    sys.stdout.write(s + \" \" * (78 - len(s)) + \"\\r\")\n",
    "    \n",
    "def lorentzian_fit(field,x0,gamma,A):\n",
    "    return A*(gamma/2)**2/((field-x0)**2+(gamma/2)**2)   \n",
    "\n",
    "def parabola_function(B,a,b,c):\n",
    "    return -a*B**2+b*B+c\n",
    "\n",
    "def lorentzian_fit_offset(field,x0,gamma,A,C):\n",
    "    return A*(gamma/2)**2/((field-x0)**2+(gamma/2)**2) + C  \n",
    "\n",
    "def center_to_edge(listlol):\n",
    "    liststep=listlol[1]-listlol[0]\n",
    "    listlol=[listlol[0]-liststep/2]+list(listlol+liststep/2)\n",
    "    return np.array(listlol)\n",
    "\n",
    "def empty_folders(path_abs):\n",
    "    walk = list(os.walk(path_abs))\n",
    "    folder_to_remove=[]\n",
    "    for path,_,path_content in walk[1:]:\n",
    "        if path_content == []:\n",
    "            folder_to_remove.append(path[-15:])\n",
    "    if len(folder_to_remove) == 0:\n",
    "        print('No empty folders in ' + path_abs)\n",
    "    else:\n",
    "        print('empty folder in ' + path_abs + '\\n',folder_to_remove)\n",
    "        \n",
    "def plot_2d_sweep(data,x=[],y=[],xlabel = '',ylabel = '',clabel = '',title = '',xtick = 'auto',ytick = 'auto',\n",
    "                  centre = None,vmin = None,vmax = None,cmap = sns.diverging_palette(240, 10, n=361),\n",
    "                  horizontal_ticks = False, fontsize = None,annot=False, flip_axis=True):\n",
    "    \"\"\"\n",
    "    Generic plotting function for 2D datasets\n",
    "    \"\"\"\n",
    "    \n",
    "    if len(x)==0: x = np.linspace(0, data.shape[1]-1, data.shape[1], dtype = int)\n",
    "    if len(y)==0: y = np.linspace(0, data.shape[0]-1, data.shape[0], dtype = int)\n",
    "    if flip_axis: fieldsweep_df = pd.DataFrame(data=np.flip(data,axis = 0),index=np.flip(y,axis = 0),columns=x)\n",
    "    else:  fieldsweep_df = pd.DataFrame(data=data,index=y,columns=x)\n",
    "    #else: fieldsweep_df = pd.DataFrame(data=data)\n",
    "    ax = sns.heatmap(fieldsweep_df, xticklabels = xtick, yticklabels = ytick,cmap = cmap,center = centre,vmin = vmin, vmax = vmax,annot=annot,annot_kws={\"fontsize\":10})#,center = -100,cmap = sns.diverging_palette(240, 10, n=361))\n",
    "    ax.collections[0].colorbar.set_label(clabel, fontsize = fontsize)\n",
    "    ax.collections[0].colorbar.ax.tick_params(labelsize=fontsize)\n",
    "    plt.tick_params(labelsize = fontsize)\n",
    "    plt.xlabel(xlabel, fontsize = fontsize)\n",
    "    plt.ylabel(ylabel, fontsize = fontsize)\n",
    "    if horizontal_ticks == True:\n",
    "        plt.xticks(rotation=0)\n",
    "        plt.yticks(rotation=0)\n",
    "    plt.title(title, fontsize = fontsize)\n",
    "\n",
    "def downsample(data,window):\n",
    "    return np.mean(data[0:len(data)-int(len(data)%window)].reshape(-1, window), axis=1)\n",
    "\n",
    "def extract_populations_4state(click_NRO, frequency_domain = False, accumulated = False, kmeans = None):\n",
    "    \"\"\"takes an array of ramsey readout clicks and, assuming there are 4 possible states, extracts the population probabilities of each\n",
    "    frequency_domain = False assumes time-domain readout and discriminates in phase\n",
    "    frequency_domain = True assumes frequency seletcive readout, discriminating along a single frequency axis\"\"\"\n",
    "    \n",
    "    # We need to do the mean or the sum over two axes for this to work, here we do the first\n",
    "    if accumulated: XY_NRO = click_NRO\n",
    "    else: XY_NRO = click_NRO.sum(-2)\n",
    "        \n",
    "    # Now we find two differential quantities allowing us to define two axes along which we will discriminate states\n",
    "    if frequency_domain:\n",
    "        delta_x = np.take(XY_NRO, 0, -1)+np.take(XY_NRO, 1, -1)-np.take(XY_NRO, 2, -1)-np.take(XY_NRO, 3, -1)\n",
    "        delta_y = np.take(XY_NRO, 0, -1)+np.take(XY_NRO, 2, -1)-np.take(XY_NRO, 1, -1)-np.take(XY_NRO, 3, -1)\n",
    "    else: \n",
    "        delta_x = np.take(XY_NRO, 0, -1)-np.take(XY_NRO, 2, -1)\n",
    "        delta_y = np.take(XY_NRO, 1, -1)-np.take(XY_NRO, 3, -1)\n",
    "    \n",
    "    # Now we take the 4 possible quadrants of the two differential quantities defined above to get 4 populations\n",
    "    q0 = np.logical_and(delta_x>0,delta_y>0).mean(0)\n",
    "    q1 = np.logical_and(delta_x>0,delta_y<0).mean(0)\n",
    "    q2 = np.logical_and(delta_x<0,delta_y>0).mean(0)\n",
    "    q3 = np.logical_and(delta_x<0,delta_y<0).mean(0)\n",
    "    \n",
    "    # If a kmeans object is passed as an argument, we use this to classify points in a 2D space instead of quadrants\n",
    "    if kmeans is not None:\n",
    "        original_shape = delta_x.shape\n",
    "\n",
    "        flatten_x = delta_x.flatten()\n",
    "        flatten_y = delta_y.flatten()\n",
    "        \n",
    "        pop = kmeans.predict(np.transpose([flatten_x, flatten_y]))\n",
    "        pop = pop.reshape(original_shape)\n",
    "        \n",
    "        q0 = (pop==0).mean(0)\n",
    "        q1 = (pop==1).mean(0)\n",
    "        q2 = (pop==2).mean(0)\n",
    "        q3 = (pop==3).mean(0)\n",
    "        \n",
    "    return(q0,q1,q2,q3,delta_x,delta_y)\n",
    "\n",
    "def fit_function(guess,func,xdata,ydata,lb = -np.inf,ub = np.inf, extend = False):\n",
    "    \"\"\"Wrapper around curve_fit to conveniently do all the stuff you wish it already did\n",
    "    returns est,std,fine,data_fit\"\"\"\n",
    "    \n",
    "    est,cov = curve_fit(func, xdata, ydata,p0 = guess, bounds = (lb, ub))\n",
    "    std = np.sqrt(np.diag(cov))\n",
    "    #print (est,std)\n",
    "    #\n",
    "    if not extend:\n",
    "        fine = np.linspace(min(xdata),max(xdata),len(xdata)*200)\n",
    "        data_fit=func(fine,*est)\n",
    "        return (est,std,fine,data_fit)\n",
    "    else:\n",
    "        delta = max(xdata) - min(xdata)\n",
    "        fine = np.linspace(min(xdata) - extend*delta,max(xdata) + extend*delta,len(xdata)*10*(2*extend+1))\n",
    "        data_fit=func(fine,*est)\n",
    "        return (est,std,fine,data_fit)\n",
    "\n",
    "def sinhspace_asymm(start,stop,npoints, nonlinearity = None): \n",
    "    \"\"\"\n",
    "    Returns a hyperbolic sinh range with the same syntax as numpy.linspace\n",
    "    points will be asymmetrically spaced, with more points at smaller values\n",
    "    nolinearity=0 gives a linear sweep.\n",
    "    Larger values enhance the number of points at values closer to zero.\n",
    "    For default nonlinearity this behaves very similar to a logarithmic sweep\n",
    "    When start==0 default nonlinearity acts similar to a lograithmic sweep over 2 orders of magnitude\n",
    "    Useful for T1, T2 and similar measurements\n",
    "    \"\"\"\n",
    "    if nonlinearity == None and start !=0: nonlinearity = np.log(abs(stop/start))\n",
    "    elif nonlinearity == None: nonlinearity = np.log(100)\n",
    "    if start==stop:\n",
    "        return(np.ones(npoints)*start)\n",
    "    elif nonlinearity!=0:\n",
    "        centre = (start+stop)/2\n",
    "        sweeprange = stop-start\n",
    "        if abs(stop)>abs(start):sweep = np.sinh(nonlinearity*sweeprange/abs(sweeprange)*np.linspace(0,1,npoints))\n",
    "        else: sweep = np.sinh(nonlinearity*sweeprange/abs(sweeprange)*np.linspace(1,0,npoints))\n",
    "        sweep = sweep-sweep[0]\n",
    "        sweep = sweep/sweep[-1]\n",
    "        sweep = start+sweeprange*sweep\n",
    "        return(sweep)\n",
    "    else:\n",
    "        return(np.linspace(start,stop,npoints))\n",
    "    \n",
    "def sinhspace(start,stop,npoints, nonlinearity = 2*np.e): \n",
    "    \"\"\"\n",
    "    Returns a hyperbolic sinh range with the same syntax as numpy.linspace\n",
    "    points will be symmetrically spaced about the middle of the range\n",
    "    nolinearity=0 gives a linear sweep.\n",
    "    Larger values enhance the number of points in the middle.\n",
    "    Useful for quickly sweeping over a resonance peak at a known location!!\n",
    "    \"\"\"\n",
    "    if start==stop:\n",
    "        return(np.ones(npoints)*start)\n",
    "\n",
    "    elif nonlinearity!=0:\n",
    "        centre = (start+stop)/2\n",
    "        sweeprange = stop-start\n",
    "        sweep = np.sinh(nonlinearity*sweeprange/abs(sweeprange)*np.linspace(-1,1,npoints))\n",
    "        sweep = centre+0.5*abs(sweeprange)*sweep/max(sweep)\n",
    "        return(sweep)\n",
    "    else:\n",
    "        return(np.linspace(start,stop,npoints))\n",
    "    \n",
    "def kmeans_plot(delta, h=1, ax=None, title=''):\n",
    "    init = np.array([\n",
    "        [ 100, 100],\n",
    "        [ 100,-100],\n",
    "        [-100, 100],\n",
    "        [-100,-100]\n",
    "    ])\n",
    "    \n",
    "    # Reshape into a good shape\n",
    "    X = np.array([delta[0].flatten(), delta[1].flatten()]).astype(int).T\n",
    "    kmeans = KMeans(n_clusters=4, random_state=0, init=init, n_init=1)\n",
    "    y_pred = kmeans.fit_predict(X)\n",
    "    \n",
    "    # construct mesh\n",
    "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "\n",
    "    # obtain labels per mesh point (reuse stored model)\n",
    "    Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    \n",
    "    # change the definition of the clusters \n",
    "    \n",
    "    if not ax:\n",
    "        plt.figure(figsize=(5,5))\n",
    "        ax = plt.gca()\n",
    "    \n",
    "    cmap = mpl.colors.ListedColormap(colors[:4])\n",
    "    norm = mpl.colors.BoundaryNorm(np.arange(4), cmap.N)\n",
    "    \n",
    "    ax.plot(delta[0],delta[1],\".\", alpha = 0.5)\n",
    "    ax.imshow(\n",
    "        Z, interpolation='nearest', cmap=cmap, alpha=0.1,\n",
    "        extent=(xx.min(), xx.max(), yy.min(), yy.max()),\n",
    "        aspect='auto', origin='lower',\n",
    "    )\n",
    "    ax.set_xlabel('Counts $\\Delta_x$')\n",
    "    ax.set_ylabel('Counts $\\Delta_y$')\n",
    "    \n",
    "    probs = []\n",
    "    for i in range(4):\n",
    "        pred = kmeans.predict(np.array(delta)[:,:,i].T)\n",
    "        P = []\n",
    "        for j in range(4): P.append((pred==j).sum()/len(pred))\n",
    "        probs.append(P)\n",
    "        # title += f\"$P_{i:d}$={P[i]:.2f},  \"\n",
    "        # if i == 1: title += '\\n'\n",
    "    # ax.set_title(title[:-2], color='k', ha ='center', fontsize='medium')\n",
    "    ax.set_title(title, color='k', ha ='center', fontsize='medium')\n",
    "    \n",
    "    return probs, kmeans\n",
    "\n",
    "def grab_timestamps_hdf(folder, start_timestamp, stop_timestamp):\n",
    "    all_timestamps = [f[:14] for f in os.listdir(folder) if 'hdf5' in f]\n",
    "    start_index = np.where(np.array(all_timestamps) == start_timestamp)[0][0]\n",
    "    stop_index  = np.where(np.array(all_timestamps) == stop_timestamp)[0][0]\n",
    "    return(all_timestamps[start_index: stop_index+1])\n",
    "\n",
    "def load_from_directory(directory, timestamp_list):\n",
    "    data_list=[]\n",
    "    for timestamp in timestamp_list:\n",
    "        filenames=getfiles_hdf5(directory)\n",
    "        for filename in filenames:\n",
    "            if timestamp in filename: \n",
    "                PrintStatic(f'Loading {filename}')\n",
    "                data=load_h5_to_dic(directory+r'\\\\'+filename)\n",
    "                data_list.append(data)\n",
    "    PrintStatic(f'Loaded {len(data_list)} files')\n",
    "    return data_list\n",
    "\n",
    "def filter_signal_band(input_signal, sampling_frequency_Hz, f_low, f_high, filter_type = 'bp', order = 10):\n",
    "    '''Takes input_signal sampled at a rate sampling_frequency_Hz\n",
    "    returns the filtered signal\n",
    "    filter function is defined by the cutoff frequencies f_low, f_high, order (int) and filter_type ('bp' or 'bs')\n",
    "    '''\n",
    "    \n",
    "    filter_function = signal.butter(order, (f_low,f_high), filter_type, fs=sampling_frequency_Hz, output='sos')\n",
    "        \n",
    "    return(signal.sosfilt(filter_function, input_signal))\n",
    "\n",
    "def filter_signal_highlow(input_signal, sampling_frequency_Hz, f_cut, filter_type = 'lp', order = 10):\n",
    "    '''Takes input_signal sampled at a rate sampling_frequency_Hz\n",
    "    returns the filtered signal\n",
    "    filter function is defined by the cutoff frequency f_cut, order (int) and filter_type ('lp' or 'hp')\n",
    "    '''\n",
    "    \n",
    "    filter_function = signal.butter(order, f_cut, filter_type, fs=sampling_frequency_Hz, output='sos')\n",
    "        \n",
    "    return(signal.sosfilt(filter_function, input_signal))\n",
    "\n",
    "def save_fig_manustyle(filename):\n",
    "    try:\n",
    "        plt.savefig(filename)\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "               \n",
    "def nphoton_labels(nphotons):\n",
    "\n",
    "    \"\"\"\n",
    "    Generates string labels for the computational basis\n",
    "    corresponding to n qubits\n",
    "    \"\"\"\n",
    "    \n",
    "    if not isinstance(nphotons, int):\n",
    "        raise ValueError('Need an integer number of qubits')\n",
    "\n",
    "    aux = []\n",
    "    ticklabels_bare = []\n",
    "    for i in range(nphotons):\n",
    "        aux.append(2)\n",
    "    for indices in np.ndindex(tuple(aux)):\n",
    "        label = ''\n",
    "        for digit in indices:\n",
    "            label += str(digit)\n",
    "        ticklabels_bare.append(label)\n",
    "    \n",
    "    ticklabels_ket = []\n",
    "    ticklabels_bra = []\n",
    "    for i in range(len(ticklabels_bare)):\n",
    "        ticklabels_ket.append('|' + ticklabels_bare[i] + '>')\n",
    "        ticklabels_bra.append('<' + ticklabels_bare[i] + '|')\n",
    "    \n",
    "    return ticklabels_ket, ticklabels_bra\n",
    "\n",
    "def is_value(obj, value):\n",
    "    try:\n",
    "        warnings.simplefilter('ignore', FutureWarning)\n",
    "        comparison = (obj == value)\n",
    "        if isinstance(comparison, np.ndarray):\n",
    "            return False\n",
    "        else:\n",
    "            return comparison\n",
    "        warnings.simplefilter('default', FutureWarning)\n",
    "    except:\n",
    "        return False\n",
    "    \n",
    "def plot_rho_3d_old(rho_exp, target = None,\n",
    "                figsize = [15, 15], fontsize = 16,\n",
    "                width = 0.5, depth = 0.5,\n",
    "                plotmag = 'real',\n",
    "                save = False,\n",
    "                title = None,\n",
    "                save_fig = True):\n",
    "    \n",
    "    nphotons = int(np.log2(rho_exp[0].shape[0]))\n",
    "    ticklabels_ket, ticklabels_bra = nphoton_labels(nphotons)\n",
    "    \n",
    "    #for i, l in enumerate(ticklabels_ket):\n",
    "    #    ticklabels_ket[i] = r'$\\vert %s \\rangle$'%(l[1:-1])\n",
    "    #    ticklabels_bra[i] = r'$\\langle %s \\vert$' % (l[1:-1])\n",
    "    ticklabels_ket = [r\"$\\vert \\uparrow\\uparrow \\rangle$\",\n",
    "                      r\"$\\vert \\uparrow\\downarrow \\rangle$\",\n",
    "                      r\"$\\vert \\downarrow\\uparrow \\rangle$\",\n",
    "                      r\"$\\vert \\downarrow\\downarrow \\rangle$\"]\n",
    "    ticklabels_bra = [r\"$\\langle \\uparrow\\uparrow \\vert$\",\n",
    "                      r\"$\\langle \\uparrow\\downarrow \\vert$\",\n",
    "                      r\"$\\langle \\downarrow\\uparrow \\vert$\",\n",
    "                      r\"$\\langle \\downarrow\\downarrow \\vert$\"]\n",
    "    \n",
    "    if not is_value(target, None):\n",
    "        assert np.shape(target) == np.shape(rho_exp), f'Need the same amount of experimental and target DMs'\n",
    "    \n",
    "    nhistograms = len(rho_exp)\n",
    "    spacedim = np.shape(rho_exp)[-1]\n",
    "\n",
    "    _x = np.arange(spacedim)\n",
    "    _y = np.arange(spacedim)\n",
    "    _xx, _yy = np.meshgrid(_x, _y)\n",
    "    x, y = _xx.ravel(), _yy.ravel()\n",
    "\n",
    "    bottom = np.zeros(spacedim**2)\n",
    "\n",
    "    rho_target_flat = []\n",
    "    if plotmag == 'real':\n",
    "        rho_target_flat = np.array(np.real(target)).flatten()\n",
    "    elif plotmag == 'abs':\n",
    "        rho_target_flat = np.array(np.abs(target)).flatten()\n",
    "                \n",
    "    \n",
    "    ticksx = np.arange(0.5, spacedim, 1)\n",
    "    ticksy = np.arange(0.6, spacedim, 1)\n",
    "\n",
    "    fig = plt.figure(figsize = (8,8))\n",
    "    ax=[]\n",
    "    ax.append(fig.add_subplot(111, projection = '3d'))\n",
    "\n",
    "\n",
    "    titles = []\n",
    "\n",
    "\n",
    "    if plotmag == 'real':\n",
    "        top = np.array(np.real(np.matrix(rho_exp)).flatten())[0]\n",
    "    elif plotmag == 'abs':\n",
    "        top = np.array(np.abs(np.matrix(rho_exp)).flatten())[0]\n",
    "    #print(len(x), len(bottom))\n",
    "    for j in np.arange(len(x)):\n",
    "        #print(j)\n",
    "        ax[0].bar3d(x[j], y[j], bottom[j], width, depth,\n",
    "                    top[j], edgecolor = cols[0], color = cols[0],\n",
    "                    alpha = 0.7, label = 'fitted')\n",
    "        if not is_value(target, None):\n",
    "            ax[0].bar3d(x[j], y[j], bottom[j], width, depth,\n",
    "                    rho_target_flat[j], edgecolor = '#404040',\n",
    "                    alpha = 0.01, label = 'target')\n",
    "    \n",
    "    if plotmag == 'abs':\n",
    "        ax[0].set_zlabel('$\\\\langle|\\\\rho|\\\\rangle$')\n",
    "    elif plotmag == 'real':\n",
    "        ax[0].set_zlabel('$\\\\langle$Re$(\\\\rho)\\\\rangle$')\n",
    "        \n",
    "    ax[0].set_title(title)\n",
    "    \n",
    "    ax[0].set_xticks(ticksx)\n",
    "    ax[0].set_yticks(ticksy)\n",
    "    ax[0].set_xticklabels(ticklabels_ket)\n",
    "    ax[0].set_yticklabels(ticklabels_bra)\n",
    "    if save_fig: save_fig_manustyle(path+\"\\\\\"+timestamp_list[0]+'_Tomography_rho_cityplot.pdf') ## THIS IS NOT GENERAL!! ONLY WORKS FOR SINGLE TIMESTAMPS!\n",
    "    plt.show()\n",
    "\n",
    "def twoqb_tomo(data, target_state):\n",
    "    \"\"\"\n",
    "    runs two qubit tomography on the input dataset and returns the best fit rho and fidelity to target state\n",
    "    target_state can be any of the following:\n",
    "    \"00+11\"\n",
    "    \"10+11\"\n",
    "    \"01+11\"\n",
    "    \"00+10+01+11\"\n",
    "    \"01\"\n",
    "    \"10\"\n",
    "    \"00\"\n",
    "    \"11\"\n",
    "    \"01+10\"\n",
    "    \"\"\"\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = np.array([p0,p1,p2,p3])\n",
    "    expectation_values = np.zeros([4,4])\n",
    "    expectation_values[0, 0] = 1\n",
    "    \n",
    "    norm = pops[3] + pops[0] + pops[1] + pops[2]\n",
    "    expectation_values[1:, 1:] = (pops[3] + pops[0] - pops[1] - pops[2])# / norm\n",
    "    \n",
    "    for i in range(3):\n",
    "        expectation_values[i+1, 0] = (- pops[3, i, i] + pops[0, i, i] + pops[1, i, i] - pops[2, i, i])\n",
    "        expectation_values[0, i+1] = (- pops[3, i, i] + pops[0, i, i] - pops[1, i, i] + pops[2, i, i])\n",
    "    \n",
    "    # Define Pauli matrices\n",
    "    I = np.eye(2)\n",
    "    sigma_x = np.array([[0, 1], [1, 0]])\n",
    "    sigma_y = np.array([[0, -1j], [1j, 0]])\n",
    "    sigma_z = np.array([[1, 0], [0, -1]])\n",
    "    \n",
    "    # Define 2-qubit Pauli basis\n",
    "    pauli_basis = [np.kron(a, b) for a in [I, sigma_x, sigma_y, sigma_z] for b in [I, sigma_x, sigma_y, sigma_z]]\n",
    "    \n",
    "    # Simulated measurement outcomes (expectation values) for demonstration\n",
    "    # Replace these with your actual measurement data\n",
    "    # Example: Expectation values in a 4x4 array corresponding to II, IX, IY, IZ, XI, XX, XY, ..., ZZ\n",
    "    measured_expectations = expectation_values\n",
    "    \n",
    "    # Flatten the expectation values to match the order of the pauli_basis list\n",
    "    measured_expectations_flat = measured_expectations.flatten()\n",
    "    \n",
    "    # Define the variable for the 2-qubit density matrix (4x4 Hermitian matrix)\n",
    "    rho = cp.Variable((4, 4), hermitian=True)\n",
    "    \n",
    "    # Objective function: Placeholder for MLE maximization\n",
    "    loss = cp.sum([(cp.real(cp.trace(pauli @ rho)) - measured_expectations_flat[i])**2 for i, pauli in enumerate(pauli_basis[:])])\n",
    "    objective = cp.Minimize(loss)\n",
    "    \n",
    "    # Constraints\n",
    "    constraints = [cp.trace(rho) == 1, rho >> 0] \n",
    "    # Add constraints for the expectation values\n",
    "    # for i, pauli in enumerate(pauli_basis[:]):\n",
    "    #     constraints.append(cp.real(cp.trace(pauli @ rho)) == measured_expectations_flat[i])\n",
    "    \n",
    "    # Setup and solve the problem\n",
    "    problem = cp.Problem(objective, constraints)\n",
    "    problem.solve()\n",
    "    rho = np.array(rho.value)\n",
    "    \n",
    "    # Calculate fidelity\n",
    "    statedict = {\n",
    "        \"00+11\":       tensor(basis(2,0), basis(2,0)) + tensor(basis(2,1), basis(2,1)),\n",
    "        \"10+11\":       tensor(basis(2,1), basis(2,0)) + tensor(basis(2,1), basis(2,1)),\n",
    "        \"01+11\":       tensor(basis(2,0), basis(2,1)) + tensor(basis(2,1), basis(2,1)),\n",
    "        \"00+10+01+11\": tensor(basis(2,0), basis(2,0)) + tensor(basis(2,0), basis(2,1)) + tensor(basis(2,1), basis(2,0)) +  tensor(basis(2,1), basis(2,1)),\n",
    "        \"01\":          tensor(basis(2,0), basis(2,1)),\n",
    "        \"10\":          tensor(basis(2,1), basis(2,0)),\n",
    "        \"00\":          tensor(basis(2,0), basis(2,0)),\n",
    "        \"11\":          tensor(basis(2,1), basis(2,1)),\n",
    "        \"01+10\":       tensor(basis(2,0), basis(2,1)) + tensor(basis(2,1), basis(2,0)),\n",
    "    }\n",
    "    \n",
    "    v = statedict[target_state]\n",
    "    v /= v.norm()\n",
    "    rho_calc = v*v.dag().full()\n",
    "    rho_calc_sqrt = sqrtm(rho_calc)\n",
    "    f = np.real(np.trace(sqrtm(rho_calc_sqrt @ rho @ rho_calc_sqrt))**2)\n",
    "    \n",
    "    return(pops,rho, f)\n",
    "\n",
    "def plot_tomo_results(pops, rho, target_state):\n",
    "    \"\"\"\n",
    "    arguments are the 4 populations, best fit density matrix and target state\n",
    "    plots matices of populations for each state, heatmaps of Re(rho) and Im(rho) and a city plot of Re(rho)\n",
    "    target_state can be any of the following:\n",
    "    \"00+11\"\n",
    "    \"10+11\"\n",
    "    \"01+11\"\n",
    "    \"00+10+01+11\"\n",
    "    \"01\"\n",
    "    \"10\"\n",
    "    \"00\"\n",
    "    \"11\"\n",
    "    \"01+10\"\n",
    "    \"\"\"\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "    cmaps = ['Blues', 'Oranges', 'Greens', 'Reds']\n",
    "    fig, axs = plt.subplots(2,2,figsize=(10,10),tight_layout=True)\n",
    "    axs = axs.flatten()\n",
    "    for i in range(4):\n",
    "        plt.sca(axs[i])\n",
    "        plot_2d_sweep(\n",
    "            pops[i], \n",
    "            x = ['X', 'Y', 'Z'],\n",
    "            y = ['X', 'Y', 'Z'],\n",
    "            clabel = \"Population\",\n",
    "            cmap = cmaps[i],\n",
    "            horizontal_ticks=True,\n",
    "            vmax=0.5,\n",
    "            vmin=0,\n",
    "            annot=True\n",
    "        )\n",
    "        axs[i].set_title('Population of '+labels[i], color=colors[i])\n",
    "    \n",
    "    statedict = {\n",
    "    \n",
    "        \"00+11\":       tensor(basis(2,0), basis(2,0)) + tensor(basis(2,1), basis(2,1)),\n",
    "        \"10+11\":       tensor(basis(2,1), basis(2,0)) + tensor(basis(2,1), basis(2,1)),\n",
    "        \"01+11\":       tensor(basis(2,0), basis(2,1)) + tensor(basis(2,1), basis(2,1)),\n",
    "        \"00+10+01+11\": tensor(basis(2,0), basis(2,0)) + tensor(basis(2,0), basis(2,1)) + tensor(basis(2,1), basis(2,0)) +  tensor(basis(2,1), basis(2,1)),\n",
    "        \"01\":          tensor(basis(2,0), basis(2,1)),\n",
    "        \"10\":          tensor(basis(2,1), basis(2,0)),\n",
    "        \"00\":          tensor(basis(2,0), basis(2,0)),\n",
    "        \"11\":          tensor(basis(2,1), basis(2,1)),\n",
    "        \"01+10\":       tensor(basis(2,0), basis(2,1)) + tensor(basis(2,1), basis(2,0)),\n",
    "    }\n",
    "    \n",
    "    v = statedict[target_state]\n",
    "    \n",
    "    \n",
    "    v /= v.norm()\n",
    "    rho_calc = v*v.dag().full()\n",
    "    rho_calc_sqrt = sqrtm(rho_calc)\n",
    "    \n",
    "    \n",
    "    plt.figure()\n",
    "    fig, axs = plt.subplots(1,2, figsize=(12,5),tight_layout=True)\n",
    "    plt.sca(axs[0])\n",
    "    plot_2d_sweep(\n",
    "        np.real(rho),\n",
    "        x = labels,\n",
    "        y = labels,\n",
    "        clabel = r\"Re($\\rho$)\",\n",
    "        cmap = 'Blues',\n",
    "        horizontal_ticks=True,\n",
    "        vmax=0.5,\n",
    "        vmin=-0,\n",
    "        annot=True,\n",
    "        flip_axis=False\n",
    "    )\n",
    "    \n",
    "    plt.sca(axs[1])\n",
    "    plot_2d_sweep(\n",
    "        np.imag(rho),\n",
    "        x = labels,\n",
    "        y = labels,\n",
    "        clabel = r\"Im($\\rho$)\",\n",
    "        cmap = 'Blues',\n",
    "        horizontal_ticks=True,\n",
    "        vmax=0.5,\n",
    "        vmin=-0.5,\n",
    "        annot=True,\n",
    "        flip_axis=False\n",
    "    )\n",
    "    \n",
    "    f = np.real(np.trace(sqrtm(rho_calc_sqrt @ rho @ rho_calc_sqrt))**2)\n",
    "    \n",
    "    ##### ACHTUNG!!! Plotting rho, NOT rho_rotated here #####\n",
    "    plot_rho_3d_old(rho, target=rho_calc, title = \"$F = %.2f$\"%f,plotmag = 'abs')\n",
    "    return()\n",
    "\n",
    "def generate_S(S: float) -> (Qobj, Qobj, Qobj):\n",
    "    mS = np.arange(S, -S-1, -1)\n",
    "    nS = len(mS)\n",
    "    S_plus = Qobj(dia_matrix((np.sqrt(S*(S+1)-mS*(mS+1)),1), shape = (nS, nS)).A)\n",
    "    S_minus = Qobj(np.transpose(S_plus))\n",
    "    Sx = Qobj(0.5*(S_plus+S_minus))\n",
    "    Sy = Qobj(-0.5*1j*(S_plus-S_minus))\n",
    "    Sz = Qobj(dia_matrix((mS,0), shape = (nS, nS)).A)\n",
    "\n",
    "    return (Sx,Sy,Sz)\n",
    "\n",
    "def is_pos_def(x):\n",
    "    return np.all(np.round(np.linalg.eigvals(np.round(x,5)),5) >= 0)\n",
    "\n",
    "def U2(theta,phi):\n",
    "    m1 = np.array(tensor(identity(2), Sz).full())\n",
    "    m2 = np.array(tensor(Sz, identity(2)).full())\n",
    "    return expm(1j*np.einsum('a,bc->abc',theta,m1)) @ expm(1j*phi*np.einsum('a,bc->abc',theta,m2))\n",
    "\n",
    "def gen_rot_matrices(thetas,phis):\n",
    "    rot_m1 = np.array(tensor(identity(2), Sz).full())\n",
    "    rot_m2 = np.array(tensor(Sz, identity(2)).full())\n",
    "    rot_m1_array = np.einsum('a,bc->abc',thetas,rot_m1)\n",
    "    rot_m2_array = np.einsum('a,bc->abc',thetas,rot_m2)\n",
    "    rot_exp1 = [list(map(expm,1j*rot_m1_array))]*len(phis)\n",
    "    # rot_exp2 = [list(map(expm,1j*phi*rot_m2_array)) for phi in phis]\n",
    "    rot_exp2 = list(map(lambda phi: list(map(expm,1j*phi*rot_m2_array)),phis))\n",
    "    return np.einsum('abcd,abde->abce',rot_exp1,rot_exp2)\n",
    "\n",
    "def fidelity_vs_angles(rho,rho_calc,thetas,phis):\n",
    "    rot_final = gen_rot_matrices(thetas,phis)\n",
    "    rho_rotated = np.einsum('abcd,abde->abce',np.einsum('abcd,de->abce',rot_final,rho),np.linalg.inv(rot_final))\n",
    "    sqrt_rho_calc = sqrtm(rho_calc)\n",
    "    m_intermediate = np.einsum('abcd,de->abce',np.einsum('de,abcd->abec',sqrt_rho_calc,rho_rotated),sqrt_rho_calc)\n",
    "    m_sqrt = np.array([[sqrtm(matrix) for matrix in array] for array in m_intermediate])\n",
    "    return np.real(np.trace(m_sqrt,axis1=2,axis2=3)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15de83a9-4e2a-47e0-8a22-2aea724f4b2a",
   "metadata": {
    "tags": [],
    "toc-hr-collapsed": true
   },
   "source": [
    "# Spectroscopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6763c45f-35b9-4f14-b7f4-cd093b8e3b02",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"Z:\\\\SMPD3-8\\\\SpinRun3\\\\Spectroscopy_vs_non_persistent_B0\\\\\"\n",
    "timestamp = \"20230828171645_\\\\\"\n",
    "\n",
    "#timestamp = \"20230728154852_\\\\\"\n",
    "\n",
    "\n",
    "file = getfiles(path+timestamp,'.hdf5')[0]\n",
    "\n",
    "data = load_h5_to_dic(path+timestamp+file)[0]\n",
    "\n",
    "B0_list   = list(data.keys())\n",
    "click     = np.array([data[B0]['click_hist'] for B0 in B0_list])\n",
    "time_axis = np.array([data[B0]['time_axis'] for B0 in B0_list])\n",
    "\n",
    "# global variable for one B0 spectroscopy --> taken with the first B0\n",
    "theta_list = data[B0_list[0]]['theta']*180/np.pi\n",
    "amplitude_list = data[B0_list[0]]['amplitude_pulse']\n",
    "\n",
    "B0list_list = np.array([np.float(string) for string in B0_list])\n",
    "\n",
    "bins = 30\n",
    "\n",
    "time_hist = time_axis.reshape(time_axis.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)*1e-6\n",
    "click_hist = click.reshape(click.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)\n",
    "\n",
    "if False:\n",
    "    for ii, B in enumerate(B0_list):\n",
    "        plt.figure()\n",
    "        plt.plot(time_hist[ii], click_hist[ii])\n",
    "        plt.xlabel(\"Time [us]\")\n",
    "        plt.ylabel(\"Counts / us\")\n",
    "        plt.title(f\"Bz = {B} mT\")\n",
    "\n",
    "clickshape=click_hist.shape[1]\n",
    "time_step=(time_hist.mean(0)[1:]-time_hist.mean(0)[:-1]).mean(0)\n",
    "\n",
    "number_of_counts_subs = (click_hist[:,:clickshape//4].sum(-1)-click_hist[:,-clickshape//4:].sum(-1))*time_step\n",
    "number_of_counts = click_hist[:,:].sum(-1)*time_step\n",
    "\n",
    "fig, (ax0, ax1) = plt.subplots(2, 1, tight_layout=True)\n",
    "fig.supxlabel(\"Bz [mT]\")\n",
    "ax0.plot(B0list_list, number_of_counts)\n",
    "ax0.set_ylabel(\"Number of counts\")\n",
    "ax1.plot(B0list_list, number_of_counts_subs)\n",
    "ax1.set_ylabel(\"Number of counts subtracted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1de9b092-ede2-40a4-b414-306c62eed78a",
   "metadata": {},
   "source": [
    "## Bx By Bz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3d320dc-9785-4977-8765-b6d2ad1d6a78",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "path = \"Z:\\\\SMPD3-8\\\\SpinRun3\\\\Spectroscopy_vs_non_persistent_B0\\\\\"\n",
    "timestamp = \"20230729141812_\\\\\" # Spectroscopy low power 420 - 450 mT\n",
    "timestamp = \"20230801095723_\\\\\" # Spectroscopy high power 440 - 450 mT\n",
    "\n",
    "file = getfiles(path+timestamp,'.hdf5')[0]\n",
    "\n",
    "data = load_h5_to_dic(path+timestamp+file)[0]\n",
    "\n",
    "B0_list   = list(data.keys())\n",
    "click     = np.array([data[B0]['click_hist'] for B0 in B0_list])\n",
    "time_axis = np.array([data[B0]['time_axis'] for B0 in B0_list])\n",
    "\n",
    "# global variable for one B0 spectroscopy --> taken with the first B0\n",
    "theta_list = data[B0_list[0]]['theta']*180/np.pi\n",
    "amplitude_list = data[B0_list[0]]['amplitude_pulse']\n",
    "\n",
    "B0list_list = np.array([np.float(string) for string in B0_list])\n",
    "\n",
    "bins = 30\n",
    "\n",
    "time_hist = time_axis.reshape(time_axis.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)*1e-6\n",
    "click_hist = click.reshape(click.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)\n",
    "\n",
    "if False:\n",
    "    for ii, B in enumerate(B0_list):\n",
    "        plt.figure()\n",
    "        plt.plot(time_hist[ii], click_hist[ii])\n",
    "        plt.xlabel(\"Time [us]\")\n",
    "        plt.ylabel(\"Counts / us\")\n",
    "        plt.title(f\"Bz = {B} mT\")\n",
    "\n",
    "clickshape=click_hist.shape[1]\n",
    "time_step=(time_hist.mean(0)[1:]-time_hist.mean(0)[:-1]).mean(0)\n",
    "\n",
    "\n",
    "number_of_counts = click_hist[:,:].sum(-1)*time_step\n",
    "number_of_counts_subs = (click_hist[:,:clickshape//4].sum(-1)-click_hist[:,-clickshape//4:].sum(-1))*time_step\n",
    "\n",
    "fig, (ax0, ax1) = plt.subplots(2, 1, tight_layout=True)\n",
    "fig.supxlabel(\"Bz [mT]\")\n",
    "ax0.plot(B0list_list, number_of_counts)\n",
    "ax0.set_ylabel(\"Number of counts\")\n",
    "ax1.plot(B0list_list, number_of_counts_subs)\n",
    "ax1.set_ylabel(\"Number of counts subtracted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ed2467e-d7d5-40cd-9250-480134de405b",
   "metadata": {},
   "outputs": [],
   "source": [
    "4e3*12/3600"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6be3de6e-6ed7-4536-afb9-f402f99c85d4",
   "metadata": {},
   "source": [
    "## B0 theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6a43cb1-654a-4ac9-8dc8-9713270debcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "keys_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ffe6f87-a52a-4a09-95a1-8b5e75fd77da",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "path = \"Z:\\\\SMPD3-8\\\\SpinRun3\\\\Spectroscopy_vs_non_persistent_B0_theta\\\\\"\n",
    "timestamp = \"20231023163444_\\\\\"#\"20230828171645_\\\\\"#\"20230827115618_\\\\\"#\"20230821200102_\\\\\"#\"20230821163834_\\\\\" # Spectroscopy high power 440 - 450 mT\n",
    "\n",
    "file = getfiles(path+timestamp,'.hdf5')[0]\n",
    "\n",
    "data = load_h5_to_dic(path+timestamp+file)[0]\n",
    "\n",
    "B0_list   = [b0_theta[:6] for b0_theta in (list(data.keys()))]\n",
    "B0_list_new = []\n",
    "for b0 in B0_list:\n",
    "    pos = b0.find('_')\n",
    "    if int(pos) != -1: B0_list_new.append(b0[:int(pos)] + '0')\n",
    "    else: B0_list_new.append(b0)\n",
    "B0_list = B0_list_new\n",
    "\n",
    "theta_list = [b0_theta[-5:] for b0_theta in (list(data.keys()))]\n",
    "keys_list = list(data.keys())\n",
    "\n",
    "click     = np.array([data[key]['click_hist'] for key in keys_list])\n",
    "time_axis = np.array([data[key]['time_axis'] for key in keys_list])\n",
    "\n",
    "# global variable for one B0 spectroscopy --> taken with the first B0\n",
    "#theta_list = data[B0_list[0]]['theta']*180/np.pi\n",
    "#amplitude_list = data[B0_list[0]]['amplitude_pulse']\n",
    "\n",
    "B0list_list = np.array([np.float(string) for string in B0_list])\n",
    "\n",
    "bins = 30\n",
    "\n",
    "time_hist = time_axis.reshape(time_axis.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)*1e-6\n",
    "click_hist = click.reshape(click.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)\n",
    "\n",
    "if False:\n",
    "    for ii, B in enumerate(B0_list):\n",
    "        plt.figure()\n",
    "        plt.plot(time_hist[ii], click_hist[ii])\n",
    "        plt.xlabel(\"Time [us]\")\n",
    "        plt.ylabel(\"Counts / us\")\n",
    "        plt.title(f\"Bz = {B} mT\")\n",
    "\n",
    "clickshape=click_hist.shape[1]\n",
    "time_step=(time_hist.mean(0)[1:]-time_hist.mean(0)[:-1]).mean(0)\n",
    "\n",
    "\n",
    "\n",
    "number_of_counts = click_hist[:,:].sum(-1)*time_step\n",
    "number_of_counts_subs = (click_hist[:,:clickshape//4].sum(-1)-click_hist[:,-clickshape//4:].sum(-1))*time_step\n",
    "\n",
    "fig, (ax0, ax1) = plt.subplots(2, 1, tight_layout=True)\n",
    "fig.supxlabel(\"Bz [mT]\")\n",
    "ax0.plot(B0list_list, number_of_counts)\n",
    "ax0.set_ylabel(\"Number of counts\")\n",
    "ax1.plot(B0list_list, number_of_counts_subs)\n",
    "ax1.set_ylabel(\"Number of counts subtracted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee53a6a3-1935-47ba-be09-88c6cc41a255",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[keys_list[0]]['theta_array'][:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "925abfe0-4f52-47ff-b08f-4cfca09fa32c",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    B0_array=data[keys_list[0]]['B0_array']\n",
    "    theta_array=data[keys_list[0]]['theta_array'][:-1]\n",
    "except:\n",
    "    print('Old format, extract B0 and theta array another way')\n",
    "\n",
    "data_2D = number_of_counts[:len(theta_array)*len(B0_array)].reshape(len(theta_array),len(B0_array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7665d150-d305-4be5-b149-42703bc32d3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "theta_array-np.pi/180*np.linspace(-0.17-0.5,21)[:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d079ca0-9d54-4d39-9e90-134784a05b7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "###### B0_array = 446.85+np.linspace(-0.03,0.04,21)\n",
    "theta_array = np.pi/180*np.linspace(-0.3,-0.08,21)\n",
    "B0_array = 447+np.linspace(-0.2,0.2,41)\n",
    "theta_array = np.pi/180*np.linspace(-0.44,-0.8,11)\n",
    "\n",
    "B0_array = 435+np.linspace(0,9,29)\n",
    "theta_array = np.pi/180*np.array([-0.6,-0.3,0.3,0.6])\n",
    "\n",
    "\n",
    "data_2D = number_of_counts[:len(theta_array)*len(B0_array)].reshape(len(theta_array),len(B0_array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "079d21dc-db41-47f1-af58-a984dad6423d",
   "metadata": {},
   "outputs": [],
   "source": [
    "B0_array = 446.85+np.linspace(-0.2,0.2,41)\n",
    "theta_array = np.pi/180*np.linspace(-0.05,0.05,51)\n",
    "theta_array = theta_array[:48]\n",
    "\n",
    "\n",
    "data_2D = number_of_counts[:len(theta_array)*len(B0_array)].reshape(len(theta_array),len(B0_array))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6588ad2-3517-4cd0-8553-a6a4a1499787",
   "metadata": {},
   "outputs": [],
   "source": [
    "25*25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efa0b121-6750-4fbe-ab00-e762850f214d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "plt.figure(figsize = (10,8))\n",
    "\n",
    "plot_2d_sweep(data_2D.T,\n",
    "              x = np.round(180*theta_array/np.pi,3),\n",
    "              y = np.round(B0_array,3),\n",
    "              xlabel = r\"$\\theta$ (°)\",\n",
    "              ylabel = r\"$B_0$ (mT)\",\n",
    "              clabel = \"Counts\",\n",
    "              cmap = 'Blues')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "446.86 -0.22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a55874b-a713-48fa-87dd-67ed4c5dbff9",
   "metadata": {},
   "outputs": [],
   "source": [
    "Spin_Gauss_sigma = 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1963dae-151c-40a8-af9b-29c64b768d71",
   "metadata": {},
   "outputs": [],
   "source": [
    "nsigma = 5\n",
    "\n",
    "\n",
    "y0 = np.zeros(200)\n",
    "y = gauss(Spin_Gauss_amplitude, 0, Spin_Gauss_sigma, nsigma*Spin_Gauss_sigma)\n",
    "y = np.concatenate((y0,y,y0))\n",
    "\n",
    "\n",
    "x = np.linspace(-nsigma*Spin_Gauss_sigma / 2, nsigma*Spin_Gauss_sigma / 2, len(y))\n",
    "\n",
    "\n",
    "x_freq=np.fft.fftfreq(len(x),d=(x[1]-x[0]))\n",
    "y_fft = np.abs(np.fft.fft(y))\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(x,y)\n",
    "\n",
    "plt.figure()\n",
    "#plt.plot(1e3*x_freq[:len(x_freq)//2+1],y_fft)\n",
    "plt.plot(1e3*x_freq,y_fft)\n",
    "#plt.gca().set(xlabel = 'freq (MHz)',ylabel = 'FFT')\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel(\"frequency (kHz)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "297d7df4-67ab-4c4e-bf6b-5d8bfc8e2c8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "plt.figure(figsize = (10,8))\n",
    "\n",
    "plot_2d_sweep(data_2D.T,\n",
    "              x = np.round(1e3*theta_array,3),\n",
    "              y = np.round(B0_array,3),\n",
    "              xlabel = r\"$\\theta/\\pi \\times 10^{-3}$ (rad)\",\n",
    "              ylabel = r\"$B_0$ (mT)\",\n",
    "              clabel = \"Counts\",\n",
    "              cmap = 'Blues')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "446.86 -0.22"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58e17c95-13fe-4060-8aef-7cfe7531a8d0",
   "metadata": {},
   "source": [
    "###  Main Er3+ line fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f67b795c-88db-4fda-8062-9d31a840d033",
   "metadata": {},
   "outputs": [],
   "source": [
    "center_b0=[]\n",
    "angles=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26522f0f-b1db-483c-aaca-d201b3bbca74",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Angle 1\n",
    "angles.append(1.0)\n",
    "\n",
    "\n",
    "%matplotlib widget\n",
    "\n",
    "path = \"Z:\\\\SMPD3-8\\\\SpinRun3\\\\Spectroscopy_vs_non_persistent_B0_theta\\\\\"\n",
    "timestamp = \"20230825173300_\\\\\"#\"20230821200102_\\\\\"#\"20230821163834_\\\\\" # Spectroscopy high power 440 - 450 mT\n",
    "\n",
    "file = getfiles(path+timestamp,'.hdf5')[0]\n",
    "\n",
    "data = load_h5_to_dic(path+timestamp+file)[0]\n",
    "\n",
    "B0_list   = [b0_theta[:6] for b0_theta in (list(data.keys()))]\n",
    "B0_list_new = []\n",
    "for b0 in B0_list:\n",
    "    pos = b0.find('_')\n",
    "    if int(pos) != -1: B0_list_new.append(b0[:int(pos)] + '0')\n",
    "    else: B0_list_new.append(b0)\n",
    "B0_list = B0_list_new\n",
    "\n",
    "theta_list = [b0_theta[-5:] for b0_theta in (list(data.keys()))]\n",
    "keys_list = list(data.keys())\n",
    "\n",
    "click     = np.array([data[key]['click_hist'] for key in keys_list])\n",
    "time_axis = np.array([data[key]['time_axis'] for key in keys_list])\n",
    "\n",
    "# global variable for one B0 spectroscopy --> taken with the first B0\n",
    "#theta_list = data[B0_list[0]]['theta']*180/np.pi\n",
    "#amplitude_list = data[B0_list[0]]['amplitude_pulse']\n",
    "\n",
    "B0list_list = np.array([np.float(string) for string in B0_list])\n",
    "\n",
    "bins = 30\n",
    "\n",
    "time_hist = time_axis.reshape(time_axis.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)*1e-6\n",
    "click_hist = click.reshape(click.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)\n",
    "\n",
    "if False:\n",
    "    for ii, B in enumerate(B0_list):\n",
    "        plt.figure()\n",
    "        plt.plot(time_hist[ii], click_hist[ii])\n",
    "        plt.xlabel(\"Time [us]\")\n",
    "        plt.ylabel(\"Counts / us\")\n",
    "        plt.title(f\"Bz = {B} mT\")\n",
    "\n",
    "clickshape=click_hist.shape[1]\n",
    "time_step=(time_hist.mean(0)[1:]-time_hist.mean(0)[:-1]).mean(0)\n",
    "\n",
    "\n",
    "number_of_counts = click_hist[:,:].sum(-1)*time_step\n",
    "number_of_counts_subs = (click_hist[:,:clickshape//4].sum(-1)-click_hist[:,-clickshape//4:].sum(-1))*time_step\n",
    "\n",
    "fig, (ax0, ax1) = plt.subplots(2, 1, tight_layout=True)\n",
    "fig.supxlabel(\"Bz [mT]\")\n",
    "ax0.plot(B0list_list, number_of_counts)\n",
    "ax0.set_ylabel(\"Number of counts\")\n",
    "ax1.plot(B0list_list, number_of_counts_subs)\n",
    "ax1.set_ylabel(\"Number of counts subtracted\")\n",
    "\n",
    "data_2D_fit = number_of_counts_subs[:len(B0_list)].reshape(len(B0_list))\n",
    "data = data_2D_fit\n",
    "y = np.array(B0_list).astype(float)\n",
    "\n",
    "popt, pcov = sp.curve_fit(lorentzian_fit, y, data, [y.mean(),(y[-1]-y[0])/5,max(data)])\n",
    "center_b0.append(popt[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9673255-eddb-4de7-ab14-8e302ac1b258",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Angle 0\n",
    "angles.append(-1)\n",
    "\n",
    "\n",
    "%matplotlib widget\n",
    "\n",
    "path = \"Z:\\\\SMPD3-8\\\\SpinRun3\\\\Spectroscopy_vs_non_persistent_B0_theta\\\\\"\n",
    "timestamp = \"20230825151511_\\\\\"#\"20230821200102_\\\\\"#\"20230821163834_\\\\\" # Spectroscopy high power 440 - 450 mT\n",
    "\n",
    "file = getfiles(path+timestamp,'.hdf5')[0]\n",
    "\n",
    "data = load_h5_to_dic(path+timestamp+file)[0]\n",
    "\n",
    "B0_list   = [b0_theta[:6] for b0_theta in (list(data.keys()))]\n",
    "B0_list_new = []\n",
    "for b0 in B0_list:\n",
    "    pos = b0.find('_')\n",
    "    if int(pos) != -1: B0_list_new.append(b0[:int(pos)] + '0')\n",
    "    else: B0_list_new.append(b0)\n",
    "B0_list = B0_list_new\n",
    "\n",
    "theta_list = [b0_theta[-5:] for b0_theta in (list(data.keys()))]\n",
    "keys_list = list(data.keys())\n",
    "\n",
    "click     = np.array([data[key]['click_hist'] for key in keys_list])\n",
    "time_axis = np.array([data[key]['time_axis'] for key in keys_list])\n",
    "\n",
    "# global variable for one B0 spectroscopy --> taken with the first B0\n",
    "#theta_list = data[B0_list[0]]['theta']*180/np.pi\n",
    "#amplitude_list = data[B0_list[0]]['amplitude_pulse']\n",
    "\n",
    "B0list_list = np.array([np.float(string) for string in B0_list])\n",
    "\n",
    "bins = 30\n",
    "\n",
    "time_hist = time_axis.reshape(time_axis.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)*1e-6\n",
    "click_hist = click.reshape(click.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)\n",
    "\n",
    "if False:\n",
    "    for ii, B in enumerate(B0_list):\n",
    "        plt.figure()\n",
    "        plt.plot(time_hist[ii], click_hist[ii])\n",
    "        plt.xlabel(\"Time [us]\")\n",
    "        plt.ylabel(\"Counts / us\")\n",
    "        plt.title(f\"Bz = {B} mT\")\n",
    "\n",
    "clickshape=click_hist.shape[1]\n",
    "time_step=(time_hist.mean(0)[1:]-time_hist.mean(0)[:-1]).mean(0)\n",
    "\n",
    "\n",
    "number_of_counts = click_hist[:,:].sum(-1)*time_step\n",
    "number_of_counts_subs = (click_hist[:,:clickshape//4].sum(-1)-click_hist[:,-clickshape//4:].sum(-1))*time_step\n",
    "\n",
    "fig, (ax0, ax1) = plt.subplots(2, 1, tight_layout=True)\n",
    "fig.supxlabel(\"Bz [mT]\")\n",
    "ax0.plot(B0list_list, number_of_counts)\n",
    "ax0.set_ylabel(\"Number of counts\")\n",
    "ax1.plot(B0list_list, number_of_counts_subs)\n",
    "ax1.set_ylabel(\"Number of counts subtracted\")\n",
    "data_2D_fit = number_of_counts_subs[:len(B0_list)].reshape(len(B0_list))\n",
    "\n",
    "\n",
    "data_2D = number_of_counts_subs[-2*len(B0_list):].reshape(len(B0_list))\n",
    "data = data_2D\n",
    "y = np.array(B0_list).astype(float)\n",
    "\n",
    "popt, pcov = sp.curve_fit(lorentzian_fit, y, data, [y.mean(),(y[-1]-y[0])/5,max(data)])\n",
    "center_b0.append(popt[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efa3069a-e812-4ee3-8ae8-7bb1f3625dc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "path = \"Z:\\\\SMPD3-8\\\\SpinRun3\\\\Spectroscopy_vs_non_persistent_B0_theta\\\\\"\n",
    "timestamp = \"20230825184024_\\\\\"#\"20230821200102_\\\\\"#\"20230821163834_\\\\\" # Spectroscopy high power 440 - 450 mT\n",
    "\n",
    "file = getfiles(path+timestamp,'.hdf5')[0]\n",
    "\n",
    "data = load_h5_to_dic(path+timestamp+file)[0]\n",
    "\n",
    "B0_list   = [b0_theta[:6] for b0_theta in (list(data.keys()))]\n",
    "B0_list_new = []\n",
    "for b0 in B0_list:\n",
    "    pos = b0.find('_')\n",
    "    if int(pos) != -1: B0_list_new.append(b0[:int(pos)] + '0')\n",
    "    else: B0_list_new.append(b0)\n",
    "B0_list = B0_list_new\n",
    "\n",
    "theta_list = [b0_theta[-5:] for b0_theta in (list(data.keys()))]\n",
    "keys_list = list(data.keys())\n",
    "\n",
    "click     = np.array([data[key]['click_hist'] for key in keys_list])\n",
    "time_axis = np.array([data[key]['time_axis'] for key in keys_list])\n",
    "\n",
    "# global variable for one B0 spectroscopy --> taken with the first B0\n",
    "#theta_list = data[B0_list[0]]['theta']*180/np.pi\n",
    "#amplitude_list = data[B0_list[0]]['amplitude_pulse']\n",
    "\n",
    "B0list_list = np.array([np.float(string) for string in B0_list])\n",
    "\n",
    "bins = 30\n",
    "\n",
    "time_hist = time_axis.reshape(time_axis.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)*1e-6\n",
    "click_hist = click.reshape(click.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)\n",
    "\n",
    "if False:\n",
    "    for ii, B in enumerate(B0_list):\n",
    "        plt.figure()\n",
    "        plt.plot(time_hist[ii], click_hist[ii])\n",
    "        plt.xlabel(\"Time [us]\")\n",
    "        plt.ylabel(\"Counts / us\")\n",
    "        plt.title(f\"Bz = {B} mT\")\n",
    "\n",
    "clickshape=click_hist.shape[1]\n",
    "time_step=(time_hist.mean(0)[1:]-time_hist.mean(0)[:-1]).mean(0)\n",
    "\n",
    "\n",
    "number_of_counts = click_hist[:,:].sum(-1)*time_step\n",
    "number_of_counts_subs = (click_hist[:,:clickshape//4].sum(-1)-click_hist[:,-clickshape//4:].sum(-1))*time_step\n",
    "\n",
    "B0_array = 435+np.linspace(0,9,29)\n",
    "theta_array = np.pi/180*np.array([-0.6,-0.3,0.3,0.6])\n",
    "\n",
    "data_2D_fit = number_of_counts_subs[:len(theta_array)*len(B0_array)].reshape(len(theta_array),len(B0_array))\n",
    "\n",
    "for ii, theta in enumerate(180*theta_array/np.pi):\n",
    "    \n",
    "    data = data_2D_fit[ii]\n",
    "    y = np.round(B0_array,3)\n",
    "\n",
    "    popt, pcov = sp.curve_fit(lorentzian_fit, y, data, [y.mean(),(y[-1]-y[0])/5,max(data)])\n",
    "    center_b0.append(popt[0])\n",
    "    angles.append(theta)\n",
    "    plot=False\n",
    "    if plot:\n",
    "        plt.figure()\n",
    "        plt.plot(y, data)\n",
    "        plt.plot(y,lorentzian_fit(y, *popt))\n",
    "        plt.xlabel(\"B0 (mT)\")\n",
    "        plt.ylabel('Subtracted counts')\n",
    "        plt.title(f'theta = {theta}')\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "c, b, a = np.polyfit(angles, center_b0, 2)\n",
    "theta_plot = np.linspace(min(angles), max(angles), 100)\n",
    "b0_fit = a + theta_plot*b + theta_plot**2*c\n",
    "\n",
    "plt.plot(angles, center_b0,'o')\n",
    "plt.plot(theta_plot, b0_fit)\n",
    "plt.xlabel(\"theta (º)\")\n",
    "plt.ylabel('Line center (mT)')\n",
    "plt.tight_layout()\n",
    "\n",
    "def gyromag_fit(theta, freq, g_para, g_perp,theta0):\n",
    "    return 7.74 / np.sqrt((g_para*np.cos(theta-theta0))**2 + (g_perp*np.sin(theta-theta0))**2)\n",
    "\n",
    "guess = [7.746,17.5,100,0]\n",
    "popt, pcov = sp.curve_fit(gyromag_fit, np.array(angles)*np.pi/180, np.array(center_b0)*1e-3, guess)\n",
    "plt.plot(theta_plot, gyromag_fit(theta_plot*np.pi/180, *popt)*1e3)\n",
    "popt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d841d90-6bee-43d7-bd10-6eec4f19532c",
   "metadata": {},
   "outputs": [],
   "source": [
    "theta_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a470292e-81da-46d7-83a7-b63c5a4f1210",
   "metadata": {},
   "outputs": [],
   "source": [
    "-1.83348946e-03*180/6.28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a81a477-effe-46cf-ad93-168f35dcd209",
   "metadata": {},
   "outputs": [],
   "source": [
    "14*8.32"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ad38f2f-252f-4e35-838f-625f6fba31be",
   "metadata": {},
   "source": [
    "## Bz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81ee53d1-a6f7-47b5-b443-52d61dfe2602",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "path = \"Z:\\\\SMPD3-8\\\\SpinRun3\\\\Spectroscopy_vs_non_persistent_BZ_with_fixed_BXBY\\\\\"\n",
    "timestamp = \"20230905175109_\\\\\" \n",
    "\n",
    "file = getfiles(path+timestamp,'.hdf5')[0]\n",
    "\n",
    "data = load_h5_to_dic(path+timestamp+file)[0]\n",
    "\n",
    "B0_list   = list(data.keys())\n",
    "click     = np.array([data[B0]['click_hist'] for B0 in B0_list])\n",
    "time_axis = np.array([data[B0]['time_axis'] for B0 in B0_list])\n",
    "\n",
    "# global variable for one B0 spectroscopy --> taken with the first B0\n",
    "theta_list = data[B0_list[0]]['theta']*180/np.pi\n",
    "amplitude_list = data[B0_list[0]]['amplitude_pulse']\n",
    "\n",
    "B0list_list = np.array([np.float(string) for string in B0_list])\n",
    "\n",
    "bins = 20\n",
    "\n",
    "time_hist = time_axis.reshape(time_axis.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)*1e-6\n",
    "click_hist = click.reshape(click.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)\n",
    "\n",
    "if False:\n",
    "    for ii, B in enumerate(B0_list):\n",
    "        plt.figure()\n",
    "        plt.plot(time_hist[ii], click_hist[ii])\n",
    "        plt.xlabel(\"Time [us]\")\n",
    "        plt.ylabel(\"Counts / us\")\n",
    "        plt.title(f\"By = {B} mT\")\n",
    "\n",
    "clickshape=click_hist.shape[1]\n",
    "time_step=(time_hist.mean(0)[1:]-time_hist.mean(0)[:-1]).mean(0)\n",
    "\n",
    "\n",
    "number_of_counts = click_hist[:,:].sum(-1)*time_step\n",
    "number_of_counts_subs = (click_hist[:,:clickshape//4].sum(-1)-click_hist[:,-clickshape//4:].sum(-1))*time_step\n",
    "\n",
    "fig, (ax0, ax1) = plt.subplots(2, 1, tight_layout=True)\n",
    "fig.supxlabel(\"Bz [mT]\")\n",
    "ax0.plot(B0list_list, number_of_counts)\n",
    "ax0.set_ylabel(\"Number of counts\")\n",
    "ax1.plot(B0list_list, number_of_counts_subs)\n",
    "ax1.set_ylabel(\"Number of counts subtracted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "643ba23c-0205-4931-b837-7ca3116045b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2309b91a-b78e-467e-9257-840e350c47bf",
   "metadata": {},
   "source": [
    "## By"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02f52017-b6c8-422f-8da8-69fafe51a48c",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"Z:\\\\SMPD3-8\\\\SpinRun3\\\\Spectroscopy_vs_non_persistent_BY_with_fixed_BZBX\\\\\"\n",
    "timestamp = \"20231024101505_\\\\\"\n",
    "\n",
    "file = getfiles(path+timestamp,'.hdf5')[0]\n",
    "    \n",
    "data = load_h5_to_dic(path+timestamp+file)[0]\n",
    "\n",
    "B0_list   = list(data.keys())\n",
    "click     = np.array([data[B0]['click_hist'] for B0 in B0_list])\n",
    "time_axis = np.array([data[B0]['time_axis'] for B0 in B0_list])\n",
    "\n",
    "\n",
    "# global variable for one B0 spectroscopy --> taken with the first B0\n",
    "theta_list = data[B0_list[0]]['theta']*180/np.pi\n",
    "amplitude_list = data[B0_list[0]]['amplitude_pulse']\n",
    "\n",
    "B0list_list = np.array([float(string) for string in B0_list])\n",
    "\n",
    "bins = 20\n",
    "\n",
    "time_hist = time_axis.reshape(time_axis.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)*1e-6\n",
    "click_hist = click.reshape(click.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)\n",
    "\n",
    "if False:\n",
    "    for ii, B in enumerate(B0_list):\n",
    "        plt.figure()\n",
    "        plt.plot(time_hist[ii], click_hist[ii])\n",
    "        plt.xlabel(\"Time [us]\")\n",
    "        plt.ylabel(\"Counts / us\")\n",
    "        plt.title(f\"Bz = {B} mT\")\n",
    "        plt.grid()\n",
    "clickshape=click_hist.shape[1]\n",
    "time_step=(time_hist.mean(0)[1:]-time_hist.mean(0)[:-1]).mean(0)\n",
    "\n",
    "\n",
    "number_of_counts = click_hist[:,:].sum(-1)*time_step\n",
    "number_of_counts_subs = (click_hist[:,:clickshape//4].sum(-1)-click_hist[:,-clickshape//4:].sum(-1))*time_step\n",
    "\n",
    "fig, (ax0, ax1) = plt.subplots(2, 1, tight_layout=True)\n",
    "\n",
    "ax0.plot(B0list_list, number_of_counts,label = \"current sweep\")\n",
    "ax0.set_ylabel(\"Number of counts\")\n",
    "ax0.grid()\n",
    "ax1.plot(B0list_list, number_of_counts_subs)\n",
    "ax1.set_ylabel(\"Number of counts subtracted\")\n",
    "ax1.grid()\n",
    "\n",
    "# ##############################################################\n",
    "# \n",
    "# timestamp = \"20230829192342_\\\\\" \n",
    "# \n",
    "# file = getfiles(path+timestamp,'.hdf5')[0]\n",
    "#     \n",
    "# data = load_h5_to_dic(path+timestamp+file)[0]\n",
    "# \n",
    "# B0_list   = list(data.keys())\n",
    "# click     = np.array([data[B0]['click_hist'] for B0 in B0_list])\n",
    "# time_axis = np.array([data[B0]['time_axis'] for B0 in B0_list])\n",
    "# \n",
    "# \n",
    "# # global variable for one B0 spectroscopy --> taken with the first B0\n",
    "# theta_list = data[B0_list[0]]['theta']*180/np.pi\n",
    "# amplitude_list = data[B0_list[0]]['amplitude_pulse']\n",
    "# \n",
    "# B0list_list = np.array([float(string) for string in B0_list])\n",
    "# \n",
    "# bins = 20\n",
    "# \n",
    "# time_hist = time_axis.reshape(time_axis.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)*1e-6\n",
    "# click_hist = click.reshape(click.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)\n",
    "# \n",
    "# if False:\n",
    "#     for ii, B in enumerate(B0_list):\n",
    "#         plt.figure()\n",
    "#         plt.plot(time_hist[ii], click_hist[ii])\n",
    "#         plt.xlabel(\"Time [us]\")\n",
    "#         plt.ylabel(\"Counts / us\")\n",
    "#         plt.title(f\"Bz = {B} mT\")\n",
    "#         plt.grid()\n",
    "# clickshape=click_hist.shape[1]\n",
    "# time_step=(time_hist.mean(0)[1:]-time_hist.mean(0)[:-1]).mean(0)\n",
    "# \n",
    "# \n",
    "# number_of_counts = click_hist[:,:].sum(-1)*time_step\n",
    "# number_of_counts_subs = (click_hist[:,:clickshape//4].sum(-1)-click_hist[:,-clickshape//4:].sum(-1))*time_step\n",
    "# \n",
    "# \n",
    "# ax0.plot(B0list_list, number_of_counts,label = \"previous sweep\")\n",
    "# \n",
    "# ax1.plot(B0list_list, number_of_counts_subs)\n",
    "# \n",
    "# \n",
    "# plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc29662e-caee-4dcc-b3d4-9387c24c87d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "path = \"Z:\\\\SMPD3-8\\\\SpinRun3\\\\Spectroscopy_vs_non_persistent_BY_with_fixed_BZBX\\\\\"\n",
    "timestamp = \"20230829223036_\\\\\"#\"20230829192342_\\\\\" \n",
    "\n",
    "file = getfiles(path+timestamp,'.hdf5')[0]\n",
    "\n",
    "data = load_h5_to_dic(path+timestamp+file)[0]\n",
    "\n",
    "B0_list   = list(data.keys())\n",
    "click     = np.array([data[B0]['click_hist'] for B0 in B0_list])\n",
    "time_axis = np.array([data[B0]['time_axis'] for B0 in B0_list])\n",
    "\n",
    "# global variable for one B0 spectroscopy --> taken with the first B0\n",
    "theta_list = data[B0_list[0]]['theta']*180/np.pi\n",
    "amplitude_list = data[B0_list[0]]['amplitude_pulse']\n",
    "\n",
    "B0list_list = np.array([np.float(string) for string in B0_list])\n",
    "\n",
    "bins = 20\n",
    "\n",
    "time_hist = time_axis.reshape(time_axis.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)*1e-6\n",
    "click_hist = click.reshape(click.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)\n",
    "\n",
    "if False:\n",
    "    for ii, B in enumerate(B0_list):\n",
    "        plt.figure()\n",
    "        plt.plot(time_hist[ii], click_hist[ii])\n",
    "        plt.xlabel(\"Time [us]\")\n",
    "        plt.ylabel(\"Counts / us\")\n",
    "        plt.title(f\"Bz = {B} mT\")\n",
    "\n",
    "clickshape=click_hist.shape[1]\n",
    "\n",
    "time_step=(time_hist.mean(0)[1:]-time_hist.mean(0)[:-1]).mean(0)\n",
    "\n",
    "\n",
    "number_of_counts = click_hist[:,:].sum(-1)*time_step\n",
    "number_of_counts_subs = (click_hist[:,:clickshape//4].sum(-1)-click_hist[:,-clickshape//4:].sum(-1))*time_step\n",
    "\n",
    "fig, (ax0, ax1) = plt.subplots(2, 1, tight_layout=True)\n",
    "fig.supxlabel(\"By [mT]\")\n",
    "ax0.plot(B0list_list, number_of_counts)\n",
    "ax0.set_ylabel(\"Number of counts\")\n",
    "ax1.plot(B0list_list, number_of_counts_subs)\n",
    "ax1.set_ylabel(\"Number of counts subtracted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2040f901-141a-415f-bbed-94531879ea43",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "path = \"Z:\\\\SMPD3-8\\\\SpinRun3\\\\Spectroscopy_vs_non_persistent_BY_with_fixed_BZBX_GaussianPulse\\\\\"\n",
    "timestamp = \"20230807140715_\\\\\"#\"20230807103500_\\\\\"#\"20230805224507_\\\\\"#\"20230805211553_\\\\\"#\"20230805212926_\\\\\"##\"20230805181703_\\\\\"#\"20230805180350_\\\\\"#\"20230805170010_\\\\\" #\"20230804193747_\\\\\"#\n",
    "\n",
    "file = getfiles(path+timestamp,'.hdf5')[0]\n",
    "\n",
    "data = load_h5_to_dic(path+timestamp+file)[0]\n",
    "\n",
    "B0_list   = list(data.keys())\n",
    "click     = np.array([data[B0]['click_hist'] for B0 in B0_list])\n",
    "time_axis = np.array([data[B0]['time_axis'] for B0 in B0_list])\n",
    "\n",
    "# global variable for one B0 spectroscopy --> taken with the first B0\n",
    "theta_list = data[B0_list[0]]['theta']*180/np.pi\n",
    "amplitude_list = data[B0_list[0]]['amplitude_pulse']\n",
    "\n",
    "B0list_list = np.array([np.float(string) for string in B0_list])\n",
    "\n",
    "bins = 20\n",
    "\n",
    "time_hist = time_axis.reshape(time_axis.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)*1e-6\n",
    "click_hist = click.reshape(click.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)\n",
    "\n",
    "if False:\n",
    "    for ii, B in enumerate(B0_list):\n",
    "        plt.figure()\n",
    "        plt.plot(time_hist[ii], click_hist[ii])\n",
    "        plt.xlabel(\"Time [us]\")\n",
    "        plt.ylabel(\"Counts / us\")\n",
    "        plt.title(f\"Bz = {B} mT\")\n",
    "\n",
    "clickshape=click_hist.shape[1]\n",
    "time_step=(time_hist.mean(0)[1:]-time_hist.mean(0)[:-1]).mean(0)\n",
    "\n",
    "\n",
    "number_of_counts = click_hist[:,:].sum(-1)*time_step\n",
    "number_of_counts_subs = (click_hist[:,:clickshape//4].sum(-1)-click_hist[:,-clickshape//4:].sum(-1))*time_step\n",
    "\n",
    "fig, (ax0, ax1) = plt.subplots(2, 1, tight_layout=True)\n",
    "fig.supxlabel(\"By [mT]\")\n",
    "ax0.plot(B0list_list, number_of_counts)\n",
    "ax0.set_ylabel(\"Number of counts\")\n",
    "ax1.plot(B0list_list, number_of_counts_subs)\n",
    "ax1.set_ylabel(\"Number of counts subtracted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fae1b2c6-4ac1-48f5-88c4-aa03ced55d4f",
   "metadata": {},
   "source": [
    "### Time resolved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b53ccc05-7774-4082-b905-c5f6d36edfea",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "path = \"Z:\\\\SMPD3-8\\\\SpinRun3\\\\Spectroscopy_vs_non_persistent_BY_with_fixed_BZBX_GaussianPulse\\\\\"\n",
    "timestamp = \"20230805225819_\\\\\"\n",
    "\n",
    "file = getfiles(path+timestamp,'.hdf5')[0]\n",
    "\n",
    "data = load_h5_to_dic(path+timestamp+file)[0]\n",
    "\n",
    "B0_list   = list(data.keys())\n",
    "click     = np.array([data[B0]['click_hist'] for B0 in B0_list])\n",
    "time_axis = np.array([data[B0]['time_axis'] for B0 in B0_list])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37e499ec-298d-4889-9359-68a4961cd5db",
   "metadata": {},
   "outputs": [],
   "source": [
    "click"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab46d319-4612-4c02-8146-d03212545052",
   "metadata": {},
   "source": [
    "# Rabi time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ba07700-288d-4063-91d4-b06ee3287b73",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\Spin_detection_calibration_Rabi_flattop\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = [\n",
    "    '20231212150344',\n",
    "    '20231212160100',\n",
    "]\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    filenames=getfiles_hdf5(path)\n",
    "    for filename in filenames:\n",
    "        if timestamp in filename: \n",
    "            print('Loading '+path+filename)\n",
    "            data=load_h5_to_dic(path+filename)\n",
    "            data_list.append(data)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f6b713b-ad1e-4aff-82da-4f680f06d040",
   "metadata": {},
   "outputs": [],
   "source": [
    "Key_field = list(data_list[0][0].keys())[0]\n",
    "time_axis=data_list[0][0][Key_field]['time_axis']\n",
    "click_array=data_list[0][0][Key_field]['click_array']\n",
    "amplitude_pulse=data_list[0][0][Key_field]['amplitude_pulse']\n",
    "rabi_times=data_list[0][0][Key_field]['rabi_times']\n",
    "\n",
    "integration_index=10\n",
    "integration_index_bg=5\n",
    "excess=((click_array.mean(0)[:,:integration_index].sum(-1)-0*click_array.mean(0)[:,-integration_index_bg:].mean(-1)*integration_index)*(time_hist[1]-time_hist[0]))\n",
    "\n",
    "excess = (excess)[1:]\n",
    "\n",
    "background = (click_array.mean(0)[:,-integration_index_bg:].mean(-1)*(time_hist[1]-time_hist[0]))[1:]\n",
    "corrected_rabi_times =(adiabatic_spin_sigma*(adiabatic_spin_Nsigma) + rabi_times)[1:]\n",
    "\n",
    "def rabi_fit(t,f,a,b):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)))\n",
    "\n",
    "def rabi_fit_heating(t,f,a,b):\n",
    "    return a*(1-np.cos(2*np.pi*f*(t)))+b*t\n",
    "\n",
    "def rabi_fit_heating_phaseshift(t,f,a,b,c,d):\n",
    "    return a*np.cos(2*np.pi*f*t+b) + c*t + d\n",
    " \n",
    "############### Plot Rabi ###############\n",
    "plt.figure(figsize=(6,6))\n",
    "\n",
    "\n",
    "try:\n",
    "    guess=[0.08, -0.12, 0, 5e-3, 0.7]#[0.05,np.ptp(excess),0.00,0,excess.mean()]\n",
    "    popt,popc=sp.curve_fit(rabi_fit_heating_phaseshift,corrected_rabi_times/1e3,excess,guess)\n",
    "    print(guess, popt)\n",
    "    label_rabi=\"$\\pi-pulse$ = %.2f us\" %(1/popt[0]/2)\n",
    "    # plt.plot(corrected_rabi_times/1e3,rabi_fit_heating_phaseshift(rabi_times[1:]/1e3,*guess), label = \"guess\")\n",
    "    plt.plot(corrected_rabi_times/1e3,rabi_fit_heating_phaseshift(corrected_rabi_times/1e3,*popt),'-',label=label_rabi)\n",
    "except Exception as e:\n",
    "    print('fit failed, ', e)\n",
    "\n",
    "\n",
    "plt.plot(corrected_rabi_times/1e3,excess,'-o')\n",
    "plt.title('average number %s \\n cycle time %.1f us \\n Spin_const_amp =  %.3f \\n amplitude_pulse = %.3f'%(average_number, cycle_time*1e3, adiabatic_spin_amp, amplitude_pulse))\n",
    "plt.xlabel('Rabi duration (us)')\n",
    "plt.ylabel('integrated excess counts')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "############### Plot Background ###############\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.plot(corrected_rabi_times/1e3,background,'-o')\n",
    "plt.title('average number %s \\n cycle time %.1f us '%(average_number, cycle_time*1e3))\n",
    "plt.xlabel('Rabi duration (us)')\n",
    "plt.ylabel('background counts')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "############### Plot decay ###############\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.plot(time_hist,click_array.mean(1).mean(0),'-o')\n",
    "plt.xlabel('time (ms)')\n",
    "plt.ylabel('count rate (/ms)')\n",
    "plt.tight_layout()\n",
    "\n",
    "def exp_decay(t,T,alpha,beta):\n",
    "    return alpha*np.exp(-t/T)+beta\n",
    "\n",
    "try:\n",
    "    popt, pcov = sp.curve_fit(exp_decay, time_hist, click_array.mean(1).mean(0),[0.5,0.5,0.1])\n",
    "    label=\"$T_1$ = %.2f ms\"%(popt[0])\n",
    "    plt.plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "    plt.legend()\n",
    "\n",
    "except Exception as e:\n",
    "    print('fit failed')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "827c4618-6f37-459f-9ee3-a08df2d054a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rabi_fit_heating_phaseshift(t,f,a,b,c,d):\n",
    "    return a*np.cos(2*np.pi*f*t+b) + c*t + d\n",
    "\n",
    "corrected_rabi_times1, excess1 = np.loadtxt('data/rabi_amp15.txt')\n",
    "amplitude_pulse = 0.15\n",
    "\n",
    "############### Plot Rabi ###############\n",
    "plt.figure(figsize=(6,6))\n",
    "\n",
    "try:\n",
    "    guess=[0.08, -0.12, 0, 5e-3, 0.7]\n",
    "    popt,popc=sp.curve_fit(rabi_fit_heating_phaseshift,corrected_rabi_times1/1e3,excess1,guess)\n",
    "    label_rabi=\"$\\pi-pulse$ = %.2f us\" %(1/popt[0]/2)\n",
    "    plt.plot(corrected_rabi_times1/1e3,rabi_fit_heating_phaseshift(corrected_rabi_times1/1e3,*popt),'-',label=label_rabi)\n",
    "except Exception as e:\n",
    "    print('fit failed, ', e)\n",
    "\n",
    "plt.plot(corrected_rabi_times1/1e3,excess1,'-o')\n",
    "plt.title('amplitude_pulse = %.3f'%(amplitude_pulse))\n",
    "plt.xlabel('Rabi duration (us)')\n",
    "plt.ylabel('integrated excess counts')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "###########################################################################\n",
    "\n",
    "\n",
    "corrected_rabi_times2, excess2 = np.loadtxt('data/rabi_amp05.txt')\n",
    "amplitude_pulse = 0.05\n",
    "\n",
    "############### Plot Rabi ###############\n",
    "plt.figure(figsize=(6,6))\n",
    "\n",
    "try:\n",
    "    guess=[0.08, -0.12, 0, 5e-3, 0.7]\n",
    "    popt,popc=sp.curve_fit(rabi_fit_heating_phaseshift,corrected_rabi_times2/1e3,excess2,guess)\n",
    "    label_rabi=\"$\\pi-pulse$ = %.2f us\" %(1/popt[0]/2)\n",
    "    plt.plot(corrected_rabi_times2/1e3,rabi_fit_heating_phaseshift(corrected_rabi_times2/1e3,*popt),'-',label=label_rabi)\n",
    "except Exception as e:\n",
    "    print('fit failed, ', e)\n",
    "\n",
    "plt.plot(corrected_rabi_times2/1e3,excess2,'-o')\n",
    "plt.title('amplitude_pulse = %.3f'%(amplitude_pulse))\n",
    "plt.xlabel('Rabi duration (us)')\n",
    "plt.ylabel('integrated excess counts')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d4f94fb-11cf-4bd5-afec-b0dd59e20556",
   "metadata": {},
   "source": [
    "# Ramsey time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fec4232-3f73-4a05-affc-8b0e8e77d6f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\SMPD3-8\\SpinRun3\\Spin_detection_Ramsey_overnight\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = ['20231005163520_\\\\']\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    directory=os.listdir(path+timestamp)[0]\n",
    "    filename=getfiles_hdf5(path+timestamp+directory)\n",
    "    data=load_h5_to_dic(path+timestamp+directory+r'\\\\'+filename[0])\n",
    "    data_list.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "baca3a16-6596-42f5-9ec9-2ac063e824e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "click_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2affd21e-7983-49ec-8608-d462e66de2b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "# -------------------\n",
    "# Functions\n",
    "# -------------------\n",
    "\n",
    "def exp_decay(t,T,alpha,beta):\n",
    "    return alpha*np.exp(-t/T)+beta\n",
    "\n",
    "def Complex_osc_decay(t,T,f,alpha,beta,phi,a):\n",
    "    return alpha/2*np.exp(-(t/T)**2-1j*(2*np.pi*f*t+2*np.pi*phi))+beta+1j*beta+(a+1j*a)*t\n",
    "\n",
    "def loss(arg):\n",
    "    data=np.array(excess[:,0])+1j*np.array(excess[:,1])\n",
    "    return np.sum(np.abs(data[:]-Complex_osc_decay(times_ramsey[:]*1e-3,*arg)))\n",
    "\n",
    "# -------------------\n",
    "# Data\n",
    "# -------------------\n",
    "Key_field = '0'\n",
    "time_axis=data_list[0][0][Key_field]['time_axis']\n",
    "click_array=data_list[0][0][Key_field]['click_array']\n",
    "BY_field=data_list[0][0][Key_field]['BY_mT']\n",
    "ramsey_detuning=data_list[0][0][Key_field]['ramsey_detuning']\n",
    "times_ramsey = data_list[0][0][Key_field]['Ramsey_time']\n",
    "\n",
    "cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "N=click_array.shape[-1]\n",
    "time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "time_step = time_hist[1]-time_hist[0]\n",
    "average_number=click_array.shape[0]\n",
    "print('shape of click array:',click_array.shape)\n",
    "\n",
    "skip_points=0\n",
    "integration_index=10\n",
    "excess=(click_array.mean(0)[:,:,skip_points:integration_index].sum(-1))*time_step\n",
    "\n",
    "# -------------------\n",
    "# Fit and Plot\n",
    "# -------------------\n",
    "\n",
    "fig = plt.figure(figsize=(10,8),tight_layout=True)\n",
    "plt.rcParams.update({'font.size': 12})\n",
    "gs0 = gridspec.GridSpec(1, 2, figure=fig,wspace=0.25,hspace=0.3,width_ratios=[1,0.5])\n",
    "gs01 = gridspec.GridSpecFromSubplotSpec(2, 1, subplot_spec=gs0[1],wspace=0,hspace=0.3)\n",
    "gs00 = gridspec.GridSpecFromSubplotSpec(3, 1, subplot_spec=gs0[0],wspace=0,hspace=0.3)\n",
    "\n",
    "\n",
    "guess=[10, ramsey_detuning*1e3 , np.max(excess[:])-np.min(excess[:]), excess[:].mean(), 0,0]\n",
    "result = sp.minimize(loss,guess)\n",
    "linear_background = result.x[5] * times_ramsey*1e-3 + result.x[3]\n",
    "times_ramsey_fit=np.linspace(times_ramsey[0],times_ramsey[-1],201)\n",
    "linear_background_fit = result.x[5] * times_ramsey_fit*1e-3 + result.x[3]\n",
    "\n",
    "label=\"%s, T_R= %.1f us \\n avg %s, BY = %s mT \\n f= %.3f MHz, df= %.3f MHz\"%(timestamp,result.x[0], average_number, BY_field, result.x[1], result.x[1]-ramsey_detuning*1e3)\n",
    "\n",
    "axe_XX=fig.add_subplot(gs00[0])\n",
    "axe_XX.plot(times_ramsey*1e-3,excess[:,0],'-o')\n",
    "axe_XX.plot(times_ramsey*1e-3,linear_background)\n",
    "axe_XX.set_title(label)\n",
    "axe_XX.set(ylabel = 'excess clicks')\n",
    "\n",
    "axe_YY=fig.add_subplot(gs00[1])\n",
    "axe_YY.plot(times_ramsey*1e-3,excess[:,1],'-o')\n",
    "axe_YY.plot(times_ramsey*1e-3,linear_background)\n",
    "axe_YY.set(ylabel = 'excess clicks')\n",
    "\n",
    "axe_XX_YY=fig.add_subplot(gs00[2])\n",
    "axe_XX_YY.plot(times_ramsey*1e-3,excess[:,0]-linear_background,'-o',color='red',alpha=0.5)\n",
    "axe_XX_YY.plot(times_ramsey_fit*1e-3,np.real(Complex_osc_decay(times_ramsey_fit*1e-3,*result.x))-linear_background_fit,color='red',label='XX')\n",
    "axe_XX_YY.plot(times_ramsey*1e-3,excess[:,1]-linear_background,'-o',color='black',alpha=0.5)\n",
    "axe_XX_YY.plot(times_ramsey_fit*1e-3,np.imag(Complex_osc_decay(times_ramsey_fit*1e-3,*result.x))-linear_background_fit,color='black',label='XY')\n",
    "axe_XX_YY.legend()\n",
    "axe_XX_YY.set(xlabel = r'Ramsey time ($\\mu$s)', ylabel = 'excess clicks')\n",
    "\n",
    "guess=[1.79,0.07,0.11]\n",
    "try:\n",
    "    popt, pcov = sp.curve_fit(exp_decay, time_hist, click_array[:n,:,:,:].mean(2).mean(1).mean(0),guess)\n",
    "    label=\"$T_1$ = %.2f ms\"%(popt[0])\n",
    "    axe_T1=fig.add_subplot(gs01[0])\n",
    "    axe_T1.plot(time_hist,click_array[:n,:,:,:].mean(2).mean(1).mean(0),'-o')\n",
    "    axe_T1.plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "    axe_T1.set(title = 'mean T1', xlabel = 'time (ms)', ylabel = r'rate $s^{-1}$')\n",
    "    axe_T1.legend()\n",
    "except:\n",
    "    print('fit failed')\n",
    "\n",
    "axe_FFT = fig.add_subplot(gs01[1])\n",
    "tau = times_ramsey*1e-3\n",
    "freq=np.fft.fftfreq(len(tau),d=(tau[1]-tau[0]))\n",
    "axe_FFT.plot(freq[1:len(tau)//2],np.abs(np.fft.fft(excess[:,0]-linear_background)[1:len(tau)//2]),'r-o')\n",
    "axe_FFT.plot(freq[1:len(tau)//2],np.abs(np.fft.fft(excess[:,1]-linear_background)[1:len(tau)//2]),'k-o')\n",
    "axe_FFT.set(xlabel = 'freq (MHz)',ylabel = 'FFT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ed3e1f4-ad4e-4f43-b914-2ce10bb907a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "10/100*60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8764867-00c8-4959-b94b-78b22e90bcb6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "slices =  5\n",
    "time_per_slice = 11/55*60\n",
    "\n",
    "popt_list = []\n",
    "for i in range(slices):\n",
    "    \n",
    "    initial = i * click_array.shape[0] // slices\n",
    "    end = (i+1) * click_array.shape[0] // slices\n",
    "    \n",
    "    skip_points=0\n",
    "    integration_index=10\n",
    "    excess=(click_array[initial:end].mean(0)[:,:,skip_points:integration_index].sum(-1))*time_step\n",
    "    \n",
    "\n",
    "    guess=[10, ramsey_detuning*1e3 , np.max(excess[:])-np.min(excess[:]), excess[:].mean(), 0,0]\n",
    "    result = sp.minimize(loss,guess)\n",
    "    linear_background = result.x[5] * times_ramsey*1e-3 + result.x[3]\n",
    "    times_ramsey_fit=np.linspace(times_ramsey[0],times_ramsey[-1],201)\n",
    "    linear_background_fit = result.x[5] * times_ramsey_fit*1e-3 + result.x[3]\n",
    "    \n",
    "    # -------------------\n",
    "    # Fit and Plot\n",
    "    # -------------------\n",
    "\n",
    "    fig = plt.figure(figsize=(10,8),tight_layout=True)\n",
    "    plt.rcParams.update({'font.size': 12})\n",
    "    gs0 = gridspec.GridSpec(1, 2, figure=fig,wspace=0.25,hspace=0.3,width_ratios=[1,0.5])\n",
    "    gs01 = gridspec.GridSpecFromSubplotSpec(2, 1, subplot_spec=gs0[1],wspace=0,hspace=0.3)\n",
    "    gs00 = gridspec.GridSpecFromSubplotSpec(3, 1, subplot_spec=gs0[0],wspace=0,hspace=0.3)\n",
    "\n",
    "\n",
    "    guess=[100, ramsey_detuning*1e3 , np.max(excess[:])-np.min(excess[:]), excess[:].mean(), 0.2, 0.2]\n",
    "    result = sp.minimize(loss,guess)\n",
    "    linear_background = result.x[5] * times_ramsey*1e-3 + result.x[3]\n",
    "    times_ramsey_fit=np.linspace(times_ramsey[0],times_ramsey[-1],201)\n",
    "    linear_background_fit = result.x[5] * times_ramsey_fit*1e-3 + result.x[3]\n",
    "    popt_list.append(result.x)\n",
    "    label=\"%s, T_R= %.1f us \\n avg %s, BY = %s mT \\n f= %.3f MHz, df= %.3f MHz\"%(timestamp,result.x[0], average_number, BY_field, result.x[1], result.x[1]-ramsey_detuning*1e3)\n",
    "\n",
    "    axe_XX=fig.add_subplot(gs00[0])\n",
    "    axe_XX.plot(times_ramsey*1e-3,excess[:,0],'-o')\n",
    "    axe_XX.plot(times_ramsey*1e-3,linear_background)\n",
    "    axe_XX.set_title(label)\n",
    "    axe_XX.set(ylabel = 'excess clicks')\n",
    "\n",
    "    axe_YY=fig.add_subplot(gs00[1])\n",
    "    axe_YY.plot(times_ramsey*1e-3,excess[:,1],'-o')\n",
    "    axe_YY.plot(times_ramsey*1e-3,linear_background)\n",
    "    axe_YY.set(ylabel = 'excess clicks')\n",
    "\n",
    "    axe_XX_YY=fig.add_subplot(gs00[2])\n",
    "    axe_XX_YY.plot(times_ramsey*1e-3,excess[:,0]-linear_background,'-o',color='red',alpha=0.5)\n",
    "    axe_XX_YY.plot(times_ramsey_fit*1e-3,np.real(Complex_osc_decay(times_ramsey_fit*1e-3,*result.x))-linear_background_fit,color='red',label='XX')\n",
    "    axe_XX_YY.plot(times_ramsey*1e-3,excess[:,1]-linear_background,'-o',color='black',alpha=0.5)\n",
    "    axe_XX_YY.plot(times_ramsey_fit*1e-3,np.imag(Complex_osc_decay(times_ramsey_fit*1e-3,*result.x))-linear_background_fit,color='black',label='XY')\n",
    "    axe_XX_YY.legend()\n",
    "    axe_XX_YY.set(xlabel = r'Ramsey time ($\\mu$s)', ylabel = 'excess clicks')\n",
    "\n",
    "    guess=[1.79,0.07,0.11]\n",
    "    try:\n",
    "        popt, pcov = sp.curve_fit(exp_decay, time_hist, click_array[:n,:,:,:].mean(2).mean(1).mean(0),guess)\n",
    "        label=\"$T_1$ = %.2f ms\"%(popt[0])\n",
    "        axe_T1=fig.add_subplot(gs01[0])\n",
    "        axe_T1.plot(time_hist,click_array[:n,:,:,:].mean(2).mean(1).mean(0),'-o')\n",
    "        axe_T1.plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "        axe_T1.set(title = 'mean T1', xlabel = 'time (ms)', ylabel = r'rate $s^{-1}$')\n",
    "        axe_T1.legend()\n",
    "    except:\n",
    "        print('fit failed')\n",
    "\n",
    "    axe_FFT = fig.add_subplot(gs01[1])\n",
    "    tau = times_ramsey*1e-3\n",
    "    freq=np.fft.fftfreq(len(tau),d=(tau[1]-tau[0]))\n",
    "    axe_FFT.plot(freq[1:len(tau)//2],np.abs(np.fft.fft(excess[:,0]-linear_background)[1:len(tau)//2]),'r-o')\n",
    "    axe_FFT.plot(freq[1:len(tau)//2],np.abs(np.fft.fft(excess[:,1]-linear_background)[1:len(tau)//2]),'k-o')\n",
    "    axe_FFT.set(xlabel = 'freq (MHz)',ylabel = 'FFT')\n",
    "\n",
    "popt_array = np.array(popt_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcf2f8a3-69ba-431b-9c7c-a716200380d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_measurement.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b895e5e3-e25b-45df-a15a-6c400afdede2",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "time_per_slice = 11/55*60\n",
    "time_measurement = np.arange(slices)*time_per_slice\n",
    "\n",
    "plt.figure()\n",
    "T2 = popt_array.T[0]\n",
    "plt.plot(time_measurement, T2, 'ko')\n",
    "plt.hlines(T2.mean(), time_measurement[0], time_measurement[-1], linestyle='dashed', color='k')\n",
    "plt.axhspan(T2.mean()-T2.std(), T2.mean()+T2.std(), color='b', alpha=0.3)\n",
    "plt.title(r'Electron $T_2^*$ = %.1f $\\pm$ %.1f µs'%(T2.mean(), T2.std()))\n",
    "plt.xlabel('Time (min)')\n",
    "plt.ylabel(r'Electron $T_2^*$ (µs)')\n",
    "plt.xlim(time_measurement[0], time_measurement[-1])\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "ramsey_frequency = 1e3*popt_array.T[1]\n",
    "plt.plot(time_measurement, ramsey_frequency, 'ko')\n",
    "plt.hlines(ramsey_frequency.mean(), time_measurement[0], time_measurement[-1], linestyle='dashed', color='k')\n",
    "plt.axhspan(ramsey_frequency.mean()-ramsey_frequency.std(), ramsey_frequency.mean()+ramsey_frequency.std(), color='r', alpha=0.3)\n",
    "plt.title(r'Frequency mean = %.1f $\\pm$ %.1f kHz'%(ramsey_frequency.mean(), ramsey_frequency.std()))\n",
    "plt.xlabel('Time (min)')\n",
    "plt.ylabel('Ramsey frequency (kHz)')\n",
    "plt.xlim(time_measurement[0], time_measurement[-1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaf15cf1-d72a-4cb2-9a04-f3e4dd076190",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "44bbee02-68ea-466a-89ff-653bf7baed7c",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# Fluorescence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e775c4c1-02ff-42f9-8176-a61a8ff6f193",
   "metadata": {},
   "source": [
    "## G2 measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "d27cd2f5-3fc8-464a-b310-6fb9ea47a588",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-29T10:04:25.178570Z",
     "iopub.status.busy": "2024-03-29T10:04:25.177570Z",
     "iopub.status.idle": "2024-03-29T10:04:25.227570Z",
     "shell.execute_reply": "2024-03-29T10:04:25.226569Z",
     "shell.execute_reply.started": "2024-03-29T10:04:25.178570Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'0': {'arrival_times': array([       4961852,       18883308,       19617048, ...,\n",
       "          20928542841888, 20928543361548, 20928545683048], dtype=int64),\n",
       "   'pulse_times': array([           436,       17026052,       34058568, ...,\n",
       "          20929970422472, 20929987450388, 20930004480604], dtype=int64),\n",
       "   'no_arrival_times': array([      29058640,       31683420,       81558648, ...,\n",
       "          20937238243792, 20937250747248, 20937303808936], dtype=int64),\n",
       "   'no_pulse_times': array([       8514388,       25542304,       42577120, ...,\n",
       "          20938408398396, 20938425433196, 20938442468012], dtype=int64),\n",
       "   'N_iterations': 470,\n",
       "   'Integration_time': 8000,\n",
       "   'gauss_duration': 1250,\n",
       "   'amplitude_pulse': 0.079}},\n",
       " {'0': ['arrival_times',\n",
       "   'pulse_times',\n",
       "   'no_arrival_times',\n",
       "   'no_pulse_times',\n",
       "   'N_iterations',\n",
       "   'Integration_time',\n",
       "   'gauss_duration',\n",
       "   'amplitude_pulse']})"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_h5_to_dic(r\"Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\spin_T1_arrival_time\\\\20240329031851_\\\\fluorescence.hdf5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "9c4bb3dd-01bb-4419-82db-e8075995f3d7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-29T10:09:06.396069Z",
     "iopub.status.busy": "2024-03-29T10:09:06.395069Z",
     "iopub.status.idle": "2024-03-29T10:09:41.790746Z",
     "shell.execute_reply": "2024-03-29T10:09:41.789746Z",
     "shell.execute_reply.started": "2024-03-29T10:09:06.395069Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'g$^2$')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "arrival_times= data[0][Key_field]['arrival_times']\n",
    "pulse_times= data[0][Key_field]['pulse_times']\n",
    "no_arrival_times= data[0][Key_field]['no_arrival_times']\n",
    "no_pulse_times= data[0][Key_field]['no_pulse_times']\n",
    "\n",
    "def calculate_arrivals_optimized(pulse_times, arrival_times, delay=0):\n",
    "    # Find the indices in \"arrival_times\" where elements of \"pulse_times\" should be inserted to maintain order.\n",
    "    inds = np.searchsorted(arrival_times, pulse_times)\n",
    "    \n",
    "    # Split the \"arrival_times\" at the positions found, which corresponds to intervals defined by \"pulse_times\"\n",
    "    return [arrival_times[inds[i]:inds[i+1]] - pulse_times[i] - delay for i in range(len(inds)-1)]\n",
    "\n",
    "def flatten(container):\n",
    "    # Function to flatten a rasted/ragged array\n",
    "    for i in container:\n",
    "        if isinstance(i, (list,tuple,np.ndarray)):\n",
    "            for j in flatten(i):\n",
    "                yield j\n",
    "        else:\n",
    "            yield i\n",
    "        \n",
    "def calculate_g2(arrivals, bins, delta_t):\n",
    "    # Calculate g2 from a rasted list of arrivals and the time bins to discretize the continous data.\n",
    "    arrival_bins = np.zeros([len(arrivals),bins])\n",
    "    \n",
    "    # For every iteration create an integer mask of the bin that the event would be placed in. \n",
    "    # eg: for events arriving at 1, 17.3 and 20.9 ms with a bin size of 1 ms the mask is [1, 17 20]\n",
    "    # Set the elements of arrival_bins for that iteration to 1, to signify an arrival\n",
    "    for i, times in enumerate(arrivals):\n",
    "        mask = (times//delta_t-1).astype(int)\n",
    "        arrival_bins[i, mask] = 1\n",
    "    # The g2 is calculated as g2_k = <n_0^j * n_k^j>_j / <n_0^j>_j * <n_k^j>_j\n",
    "    # https://iramis.cea.fr/spec/Pres/Quantro/static/wp-content/uploads/2010/10/thesis-book_ZW.pdf page 98\n",
    "    return (arrival_bins[:,0]*arrival_bins.T).mean(1) / (arrival_bins[:,0].mean() * arrival_bins.mean(0))\n",
    "\n",
    "# average_number=res.pulse_timestamp.count_so_far()\n",
    "# print(average_number)\n",
    "\n",
    "# arrival_times = np.array([x[0] for x in res.pulse_timestamp.fetch_all()])\n",
    "# pulse_times = np.array([x[0] for x in res.pulse_start.fetch_all()])\n",
    "\n",
    "# no_arrival_times = np.array([x[0] for x in res.no_pulse_timestamp.fetch_all()])\n",
    "# no_pulse_times = np.array([x[0] for x in res.no_pulse_start.fetch_all()])\n",
    "\n",
    "\n",
    "# Calculate the arrival time after sending a pulse and not sending a pulse.\n",
    "# The result is a list of arrays, each array corresponds to a different iteration of the experiment. \n",
    "# The size of the array is the number of detections and the values the time after the sequence started\n",
    "# eg. if 2 photons arrive at 1 and 3.5 ms the array for that iteration would be [1000000, 3500000], if no photon arrives then []\n",
    "ragged_arrivals = calculate_arrivals_optimized(pulse_times, arrival_times)\n",
    "ragged_no_arrivals = calculate_arrivals_optimized(no_pulse_times, no_arrival_times)\n",
    "\n",
    "# Flatten the arrays and plot the histograms\n",
    "arrivals = np.sort(list(flatten(ragged_arrivals)))\n",
    "no_arrivals = np.sort(list(flatten(ragged_no_arrivals)))\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.hist(arrivals*1e-6, color='r', alpha=0.7, bins=20)\n",
    "plt.hist(no_arrivals*1e-6, color='k', alpha=0.7, bins=20)\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel('Counts')\n",
    "\n",
    "\n",
    "# Calculate the discretization of the points and g2\n",
    "t0, t1 = min(arrivals), max(arrivals)\n",
    "bins = 20\n",
    "bin_size = (t1-t0)/bins\n",
    "time_bins = np.arange(bins)*bin_size*1e-6\n",
    "\n",
    "g2 = calculate_g2(ragged_arrivals, bins, bin_size)\n",
    "no_g2 = calculate_g2(ragged_no_arrivals, bins, bin_size)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.plot(time_bins[1:-1], g2[1:-1], 'ro')\n",
    "plt.plot(time_bins[1:-1], no_g2[1:-1], 'ko')\n",
    "plt.hlines(1, 0, time_bins[-1], linestyle='--', color='gray', alpha=0.5)\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel(f'g$^2$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c40d705-385f-4f7a-949d-92c6c151da60",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:/SMPD3-8/SpinRun3-1/spin_T1'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = ['20240205180740_\\\\']\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    directory=os.listdir(path+timestamp)[0]\n",
    "    filename=getfiles_hdf5(path+timestamp+directory)\n",
    "    data=load_h5_to_dic(path+timestamp+directory+r'\\\\'+filename[0])\n",
    "    data_list.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e00f7911-4ae2-4c91-a0ff-87c02a226b28",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_axis=data_list[1][0][Key_field]['time_axis']\n",
    "data_prep0=data_list[0][0][Key_field]['click_preparation_array']\n",
    "data_prep1=data_list[1][0][Key_field]['click_preparation_array']\n",
    "N = 30\n",
    "\n",
    "time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "time_step = (time_axis[1:] - time_axis[:-1]).mean()*1e-6\n",
    "avg_rate0 = data_prep0.mean(0).mean(0) / time_step\n",
    "avg_rate1 = data_prep1.mean(0).mean(0) / time_step\n",
    "\n",
    "fig,ax=plt.subplots(1,1, figsize = (10,5))\n",
    "\n",
    "guess=[0.1,0.025,0.1]\n",
    "try:\n",
    "    popt1,popc1=sp.curve_fit(exp_decay,time_hist[1:],avg_rate0[1, 1:],guess)\n",
    "    label=r\"prep ${|\\downarrow\\rangle}_n$ | T1 = %.2f ms | eff = %.3f\"%(popt1[0], popt1[0]*popt1[1])\n",
    "    plt.plot(time_hist,exp_decay(time_hist,*popt1),'b-',label=label,alpha=0.5)\n",
    "    \n",
    "    popt2,popc2=sp.curve_fit(exp_decay,time_hist[1:],avg_rate1[0, 1:],guess)\n",
    "    label=r\"prep ${|\\downarrow\\rangle}_n$ | T1 = %.2f ms | eff = %.3f\"%(popt2[0], popt2[0]*popt2[1])\n",
    "    plt.plot(time_hist,exp_decay(time_hist,*popt2),'r-',label=label,alpha=0.5)\n",
    "except Exception as e:\n",
    "    print('fit failed', e)\n",
    "    \n",
    "\n",
    "ax.plot(time_hist[1:], avg_rate0[1, 1:], 'bo')\n",
    "ax.plot(time_hist[1:], avg_rate1[0, 1:], 'ro')\n",
    "ax.set_xlabel(\"Time [ms]\")\n",
    "ax.set_ylabel(\"Count rate [1/ms]\")\n",
    "ax.set_ylim([0, np.max(avg_rate0)*1.2])\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faafa5fd-92ed-45bb-8642-d1030b05280d",
   "metadata": {},
   "source": [
    "## raman heating vs amplitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3aee1e1-4174-434f-8175-4652327c1bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:/SMPD3-8/SpinRun3-1/raman_heating/'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = ['20240206121310_\\\\']\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    filename=getfiles_hdf5(path+timestamp)\n",
    "    data=load_h5_to_dic(path+timestamp+r'\\\\'+filename[0])\n",
    "    data_list.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd59750e-e4a9-4974-a89a-d22dc36b15a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_axis=data_list[0][0][Key_field]['time_axis']\n",
    "click_array=data_list[0][0][Key_field]['click_array']\n",
    "factors = np.linspace(0.2,2,10)\n",
    "N = 30\n",
    "\n",
    "time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "\n",
    "x = (106 * lorentz_filter(400, 700)* factors)**2/400 * 70/800\n",
    "\n",
    "a = click_array.mean(0)\n",
    "plt.figure(figsize = (6,5))\n",
    "plot_2d_sweep(a, \n",
    "              x = time_hist.astype(int),\n",
    "              y = np.round(x, 2),\n",
    "              xlabel = \"Time (ms)\",\n",
    "              ylabel = r\"$\\Omega_{Raman}$ (kHz)\",\n",
    "              clabel = \"Count rate (ms$^{-1}$)\",\n",
    "              cmap = \"Blues\",\n",
    "              horizontal_ticks=True,\n",
    "              xtick=5\n",
    "             )\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "est_list=[]\n",
    "for i in range(a.shape[0]):\n",
    "    data_decay = a[i]\n",
    "    plt.plot(time_hist,data_decay,'o')\n",
    "    try:\n",
    "        popt, pcov = sp.curve_fit(exp_decay, time_hist, data_decay, [0.1,0.1,0.05])\n",
    "        label=\"T1 = %.2f ms\"%(popt[0])\n",
    "        plt.plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "        est_list.append(popt)\n",
    "        plt.legend()\n",
    "        plt.xlabel('Time (ms)')\n",
    "        plt.ylabel('Count rate (1/ms)')\n",
    "    except Exception as e:\n",
    "        print('fit failed: ',e)\n",
    "est_list = np.array(est_list)\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "x=np.round(factors,2)\n",
    "plt.plot(x, est_list[:, 0])\n",
    "plt.xlabel(\"Cooldown time (ms)\")\n",
    "plt.ylabel(\"Estimated decay rate (1/ms)\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cfd2b87-c68b-4232-aa4c-7d03507dafd9",
   "metadata": {},
   "source": [
    "## raman heating vs duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a4d4575-abc2-4b11-a9af-4da035adcbac",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:/SMPD3-8/SpinRun3-1/raman_heating/'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = ['20240206124805_\\\\']\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    filename=getfiles_hdf5(path+timestamp)\n",
    "    data=load_h5_to_dic(path+timestamp+r'\\\\'+filename[0])\n",
    "    data_list.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79d6b722-1954-45e3-90cd-eaedb0707607",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_axis=data_list[0][0][Key_field]['time_axis']\n",
    "click_array=data_list[0][0][Key_field]['click_array']\n",
    "raman_pulse_durations = (1e3+1e6*np.linspace(0,10,21))*1e-6\n",
    "N = 30\n",
    "\n",
    "time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "\n",
    "x = raman_pulse_durations\n",
    "\n",
    "a = click_array.mean(0)\n",
    "plt.figure(figsize = (6,5))\n",
    "plot_2d_sweep(a, \n",
    "              x = time_hist.astype(int),\n",
    "              y = np.round(x, 2),\n",
    "              xlabel = \"Time (ms)\",\n",
    "              ylabel = r\"Pulse duration (ms)\",\n",
    "              clabel = \"Count rate (ms$^{-1}$)\",\n",
    "              cmap = \"Blues\",\n",
    "              horizontal_ticks=True,\n",
    "              xtick=5\n",
    "             )\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "est_list=[]\n",
    "for i in range(a.shape[0]):\n",
    "    data_decay = a[i]\n",
    "    plt.plot(time_hist,data_decay,'o')\n",
    "    try:\n",
    "        popt, pcov = sp.curve_fit(exp_decay, time_hist, data_decay, [0.1,0.1,0.05])\n",
    "        label=\"f = %.2f\"%(x[i])\n",
    "        plt.plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "        est_list.append(popt)\n",
    "        plt.legend()\n",
    "        plt.xlabel('Time (ms)')\n",
    "        plt.ylabel('Count rate (1/ms)')\n",
    "    except Exception as e:\n",
    "        print('fit failed: ',e)\n",
    "est_list = np.array(est_list)\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "popt, pcov = sp.curve_fit(exp_decay, x, a[:,0], [0.1,-0.1,0.05])\n",
    "plt.plot(x, a[:,0])\n",
    "plt.plot(x, exp_decay(x,*popt), label=\"T1 = %.2f ms\"%(popt[0]))\n",
    "plt.legend()\n",
    "plt.xlabel(\"Pulse duration  (ms)\")\n",
    "plt.ylabel(\"Count rate at 0 ms (1/ms)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48b132f3-ca17-40a5-aef3-49362daec02e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31c98236-ec47-4f94-8d3d-59f3d8abe5da",
   "metadata": {},
   "source": [
    "# Selective fluorescence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fde06d8-cd58-4ad9-a856-4a82fa41ef61",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fd57599-3c62-4064-9563-cab0f70853d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3\\\\Selective_fluorescence\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = ['20231003163847_\\\\', '20231003180145_\\\\']\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    directory=os.listdir(path+timestamp)[0]\n",
    "    filename=getfiles_hdf5(path+timestamp+directory)\n",
    "    data=load_h5_to_dic(path+timestamp+directory+r'\\\\'+filename[0])\n",
    "    data_list.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3568e86b-1292-4455-b300-f709e192a7ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "(time_axis[1:]-time_axis[:-1]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "584b63a0-872b-4172-8d0d-0593f640c289",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_axis=data_list[1][0][Key_field]['time_axis']\n",
    "data_prep0=data_list[0][0][Key_field]['click_preparation_array']\n",
    "data_prep1=data_list[1][0][Key_field]['click_preparation_array']\n",
    "N = 30\n",
    "\n",
    "time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "time_step = (time_axis[1:] - time_axis[:-1]).mean()*1e-6\n",
    "avg_rate0 = data_prep0.mean(0).mean(0) / time_step\n",
    "avg_rate1 = data_prep1.mean(0).mean(0) / time_step\n",
    "\n",
    "fig,ax=plt.subplots(1,1, figsize = (10,5))\n",
    "\n",
    "guess=[0.1,0.025,0.1]\n",
    "try:\n",
    "    popt1,popc1=sp.curve_fit(exp_decay,time_hist[1:],avg_rate0[1, 1:],guess)\n",
    "    label=r\"prep ${|\\downarrow\\rangle}_n$ | T1 = %.2f ms | eff = %.3f\"%(popt1[0], popt1[0]*popt1[1])\n",
    "    plt.plot(time_hist,exp_decay(time_hist,*popt1),'b-',label=label,alpha=0.5)\n",
    "    \n",
    "    popt2,popc2=sp.curve_fit(exp_decay,time_hist[1:],avg_rate1[0, 1:],guess)\n",
    "    label=r\"prep ${|\\downarrow\\rangle}_n$ | T1 = %.2f ms | eff = %.3f\"%(popt2[0], popt2[0]*popt2[1])\n",
    "    plt.plot(time_hist,exp_decay(time_hist,*popt2),'r-',label=label,alpha=0.5)\n",
    "except Exception as e:\n",
    "    print('fit failed', e)\n",
    "    \n",
    "\n",
    "ax.plot(time_hist[1:], avg_rate0[1, 1:], 'bo')\n",
    "ax.plot(time_hist[1:], avg_rate1[0, 1:], 'ro')\n",
    "ax.set_xlabel(\"Time [ms]\")\n",
    "ax.set_ylabel(\"Count rate [1/ms]\")\n",
    "ax.set_ylim([0, np.max(avg_rate0)*1.2])\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1dd66e32-73af-4a93-b902-c895346b10aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "kappa = 4.41e6\n",
    "delta = 300e3 * 2 * np.pi\n",
    "T1 = 1.1e-3\n",
    "gamma_P = 1 / (T1 * 2*np.pi)\n",
    "OmegaR_05 = 50e3 * 2 * np.pi # Rabi frequency at 0.05 relative power. see notebook AC Stark shift\n",
    "\n",
    "g_0 = np.sqrt(kappa * gamma_P) / 2\n",
    "R = kappa * OmegaR_05 / (2 * g_0 * 0.05) #\n",
    "epsilon = R * np.linspace(0, 0.02, 10)\n",
    "\n",
    "chi = 2 * g_0 ** 2 / delta # Hz\n",
    "nbar = epsilon**2 / (delta**2 + kappa**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "081eaee7-01c0-435f-9479-da6fd24f64c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "g_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b71946a-0fbb-446d-a262-b6552334c9aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "4.4e6**2 / (4.4e6**2 + (2*np.pi*40e3)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f570731e-aefd-4b94-81b0-80d3d1150f2f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5fa580d8-2ac1-45b7-acc3-d70bef21f27b",
   "metadata": {},
   "source": [
    "# Time Fluorescence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd51d04f-9d86-48d8-ae8f-20043b46d643",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3\\\\spin_T1_timestamp\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = [\"20231006170058_\",\"20231006170748_\"]#[\"20231006162059_\", \"20231006162602_\"]#['20231006153154_\\\\', '20231006160956_\\\\']\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    filename=getfiles_hdf5(path+timestamp)\n",
    "    data=load_h5_to_dic(path+timestamp+r'\\\\'+filename[0])\n",
    "    data_list.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3856205e-f3e1-4951-a1b9-feffe472f46c",
   "metadata": {},
   "outputs": [],
   "source": [
    "click_list0=data_list[0][0][Key_field]['click_list']\n",
    "click_list1=data_list[1][0][Key_field]['click_list']\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "bins = np.linspace(50, np.max(click_list1), 100)\n",
    "time_hist = (bins[1:] + bins[:-1])/2\n",
    "time_hist_fit = np.linspace(time_hist[0], time_hist[-1], 1001)\n",
    "\n",
    "dc_counts, _ = np.histogram(click_list0, bins=bins)\n",
    "spin_dc_counts, _ = np.histogram(click_list1, bins=bins)\n",
    "spin_counts = spin_dc_counts - dc_counts\n",
    "\n",
    "guess_dc = [100,800,200]\n",
    "popt_dc, _ = sp.curve_fit(exp_decay, time_hist, dc_counts, guess_dc)\n",
    "\n",
    "guess_spin = [1000,400,0]\n",
    "popt_spin, _ = sp.curve_fit(exp_decay, time_hist, spin_counts, guess_spin)\n",
    "\n",
    "plt.figure(figsize = (12,5))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(time_hist, dc_counts, 'ko',  label=r'$|g\\rangle, T_{1 res}$ = %.2f µs'%popt_dc[0], alpha=0.5)\n",
    "plt.plot(time_hist_fit, exp_decay(time_hist_fit, *popt_dc), 'k')\n",
    "plt.plot(time_hist, spin_counts, 'ro', label=r'$|e\\rangle - |g\\rangle, T_{1 spin} = %.2f$ µs'%popt_spin0][, alpha=0.5)\n",
    "plt.plot(time_hist_fit, exp_decay(time_hist_fit, *popt_spin), 'r')\n",
    "plt.legend()\n",
    "plt.xlabel('Time (µs)')\n",
    "plt.ylabel('Counts')\n",
    "plt.xlim(0,None)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(time_hist, dc_counts, 'ko', label=r'$|g\\rangle, T_{1 res}$ = %.2f µs'%popt_dc[0], alpha=0.5)\n",
    "plt.plot(time_hist_fit, exp_decay(time_hist_fit, *popt_dc), 'k')\n",
    "plt.plot(time_hist, spin_counts, 'ro', label=r'$|e\\rangle - |g\\rangle, T_{1 spin} = %.2f$ µs'%popt_spin[0], alpha=0.5)\n",
    "plt.plot(time_hist_fit, exp_decay(time_hist_fit, *popt_spin), 'r')\n",
    "\n",
    "plt.xlabel('Time (µs)')\n",
    "plt.ylabel('Counts')\n",
    "\n",
    "plt.xlim(0,500)\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2db598de-7a07-41cf-b671-7352602aa024",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# Quantum jumps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c0a9278-1176-4562-985f-4b84d0a137ef",
   "metadata": {},
   "source": [
    "## Nuclear spin readout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be45fb74-1c4c-481f-91d2-564b3761e2ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "def chop_clicks(click_array,chunk_size, time_hist,integration_weights):\n",
    "    npts = np.round(click_array.shape[0],-2)\n",
    "    offset = 0\n",
    "    excess_array = []\n",
    "    \n",
    "    for start in offset+np.linspace(0,npts-chunk_size,npts//chunk_size,dtype = int):\n",
    "\n",
    "        stop = start+chunk_size\n",
    "        \n",
    "        integration_index=5\n",
    "        integration_index_bg=5\n",
    "        \n",
    "        #print(click_array.shape)\n",
    "        \n",
    "        excess=(np.dot(click_array[start:stop].mean(0),integration_weights))*(time_hist[1]-time_hist[0])\n",
    "        \n",
    "        \n",
    "        excess_array.append(excess)\n",
    "        \n",
    "        background = click_array.mean(0)[:,-integration_index_bg:].mean(-1)*(time_hist[1]-time_hist[0])\n",
    "    \n",
    "    excess_array = np.array(excess_array)\n",
    "    points_array = chunk_size/2+np.linspace(0,npts-chunk_size,npts//chunk_size,dtype = int)\n",
    "    \n",
    "    return excess_array, points_array\n",
    "\n",
    "def PrintStatic(s):\n",
    "    sys.stdout.flush()\n",
    "    sys.stdout.write(s + \" \" * (78 - len(s)) + \"\\r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6ed2cb5-2eea-48e4-ac00-be0424eaeff7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def exp_decay(t,T,alpha,beta):\n",
    "    return alpha*np.exp(-t/T)+beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31858244-59f7-4e77-862a-995c50de90b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def exp_decay_weights(time_hist,PurcellT1, plot = False):\n",
    "    \"\"\"Returns optimal integration weights for a given time interval and T1 decay time\n",
    "    PurcellT1 is given in seconds\n",
    "    time_hist is given in ms (to keep it the same array as defined in the rest of the code)\"\"\"\n",
    "    \n",
    "    weights = exp_decay(time_hist,PurcellT1*1e3,2,0)\n",
    "    if plot: \n",
    "        plt.figure()\n",
    "        plt.plot(time_hist,weights)\n",
    "        plt.xlabel('Time (ms)')\n",
    "        plt.ylabel('Weight')\n",
    "    \n",
    "    return(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8894746-5d18-4150-bdb3-4b9c033ac424",
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File(filepath+filename, \"r\") as f:\n",
    "    print(np.array(f[str(0)]['centre_freq']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a36bd29-43dd-4143-b037-46459c6cb437",
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = \"Z:\\\\SMPD3-8\\\\SpinRun3\\\\Spectroscopy_frequency_sweep_fixed_field\\\\20230810191945_\\\\20230810191945_Spectroscopy_frequency_sweep_fixed_field_amp0.007\\\\\"\n",
    "filename = \"20230810191945_Spectroscopy_frequency_sweep_fixed_field_amp0.007_state_disc.hdf5\"\n",
    "\n",
    "chunk_size = 1000\n",
    "PurcellT1 = 0.6e-3 # Purcell T1 of the electron spin. This sets the optimal weights function\n",
    "\n",
    "\n",
    "x = []\n",
    "y1 = []\n",
    "y2 = []\n",
    "centre_freqs = []\n",
    "\n",
    "with h5py.File(filepath+filename, \"r\") as f:\n",
    "    for index in range(500):\n",
    "        \n",
    "        PrintStatic(str(index))\n",
    "        \n",
    "        centre_freqs.append(np.array(f[str(index)]['centre_freq']))\n",
    "        click_array = np.array(f[str(index)]['click_array'])\n",
    "        N = (click_array.shape[-1])\n",
    "        time_data = np.array(f[str(index)]['time_axis'])\n",
    "        time_axis=time_data-time_data[0]\n",
    "        cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "        time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "        if index ==0: \n",
    "            integration_weights = exp_decay_weights(time_hist,PurcellT1, plot = True)\n",
    "            # integration_weights = np.ones_like(integration_weights)\n",
    "            # integration_weights[10:] = 0\n",
    "\n",
    "            #plt.figure()\n",
    "            plt.plot(time_hist,integration_weights)\n",
    "            plt.xlabel('Time (ms)')\n",
    "            plt.ylabel('Weight')\n",
    "        \n",
    "        excess_array, points_array = chop_clicks(click_array,chunk_size,time_hist,integration_weights)\n",
    "        x  = np.concatenate((x ,index*points_array[-1]+points_array))\n",
    "        y1 = np.concatenate((y1,excess_array[:,0]))\n",
    "        y2 = np.concatenate((y2,excess_array[:,1]))\n",
    "        \n",
    "print(excess_array.shape)        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1a65626-272f-4293-a344-b112b95bcbfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.linspace(0,max(x),len(centre_freqs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d63bd1b-0b4c-4788-9d52-5b8c574dffe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = 0//chunk_size\n",
    "stop  = 70000//chunk_size\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print(\"SNR = %.3f\" %(np.std(y2[start:stop])/np.mean(y2[start:stop])))\n",
    "\n",
    "\n",
    "plt.figure(figsize = (22,12))\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "\n",
    "plt.plot(x,y1,label = r\"$|\\uparrow \\rangle$\")\n",
    "plt.plot(x,y2,label = r\"$|\\downarrow \\rangle$\")\n",
    "\n",
    "# plt.plot(x[start:stop],y1[start:stop])\n",
    "# plt.plot(x[start:stop],y2[start:stop])\n",
    "plt.xlim(0,max(x))\n",
    "\n",
    "plt.xlabel('Shot')\n",
    "plt.ylabel('Integrated excess counts')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "\n",
    "plt.plot(np.linspace(0,max(x),len(centre_freqs)),centre_freqs)\n",
    "plt.xlabel('Shot')\n",
    "plt.ylabel('Spin drive offset frequency (kHz)')\n",
    "plt.xlim(0,max(x))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(filepath+'Nuclear_spin_flips.pdf')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e1762ff-61a8-4934-a6f1-fb66e7acebff",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean(y2)/np.mean(y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a57d275-06c2-4401-a119-8e49763b6718",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad8eef02-9a78-4688-b2a3-eeba3cc98b2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = (7,8))\n",
    "\n",
    "plt.plot(y1[np.where(y2<0.12)][:2000])\n",
    "plt.plot(y2[np.where(y1<0.12)][:2000])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b5ef8de-91e8-477d-a3cd-6ea3ca5104c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "11/8 * (22-8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84918681-7034-41d5-91c9-51267e34fb25",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = (7,8))\n",
    "\n",
    "scale = y2[np.where(y1<0.12)][:2000].mean()/y1[np.where(y2<0.12)][:2000].mean()\n",
    "\n",
    "\n",
    "plt.scatter(y1*scale,y2, s = 5)\n",
    "\n",
    "diag = np.linspace(0,0.3,1001)\n",
    "plt.plot(diag,diag,\"--\", color = 'dimgrey')\n",
    "\n",
    "plt.xlim(0,0.3)\n",
    "plt.ylim(0,0.3)\n",
    "\n",
    "\n",
    "plt.xlabel('Integrated excess counts, low freq line')\n",
    "plt.ylabel('Integrated excess counts, high freq line')\n",
    "\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b884edd-6007-4132-8533-0b950510e8f5",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# Nuclear Rabi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b829df2-23e4-4fdb-8d89-bc0bca123714",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3\\\\Nuclear_Rabi_duration_flattop\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = ['20231010030142_\\\\']\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    directory=os.listdir(path+timestamp)[0]\n",
    "    filename=getfiles_hdf5(path+timestamp+directory)\n",
    "    data=load_h5_to_dic(path+timestamp+directory+r'\\\\'+filename[0])\n",
    "    data_list.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2debb2f8-54c2-42ab-8b2d-da24d1bbe54d",
   "metadata": {},
   "outputs": [],
   "source": [
    "Key_field = '0'\n",
    "data=data_list[0][0][Key_field]['click_array']\n",
    "data_prep=data_list[0][0][Key_field]['click_preparation_array']\n",
    "rabi_durations = data_list[0][0][Key_field]['rabi_durations']\n",
    "corrected_rabi_durations = adiabatic_spin_sigma*(adiabatic_spin_Nsigma)+rabi_durations # Effective pulse duration including sigmoids\n",
    "\n",
    "fig,ax=plt.subplots(3,1, figsize=(11, 12), tight_layout=True)\n",
    "hist=ax[0].hist(np.concatenate(data.sum(2)[:,:,0]-data.sum(2)[:,:,1]), bins=np.arange(-40,40,2),alpha=0.5, label='rabi')\n",
    "hist=ax[0].hist(np.concatenate(data_prep.sum(2)[:,:,0]-data_prep.sum(2)[:,:,1]), bins=np.arange(-40,40,2),alpha=0.5, label='preparation')\n",
    "ax[0].vlines(0,0,max(hist[0]))\n",
    "\n",
    "ax[0].set_xlabel('delta clicks during NuclearSpin RO')\n",
    "ax[0].set_ylabel('counts')\n",
    "ax[0].legend()\n",
    "\n",
    "ax[1].plot(corrected_rabi_durations*1e-3, data.mean(2).mean(0)[:,0], label = r\"${|\\uparrow\\rangle}$\")\n",
    "ax[1].plot(corrected_rabi_durations*1e-3, data.mean(2).mean(0)[:,1], label = r\"${|\\downarrow\\rangle}$\")\n",
    "ax[1].plot(corrected_rabi_durations*1e-3, data_prep.mean(2).mean(0)[:,0], label =   r\"P$_{|\\uparrow\\rangle}$, prepare $|\\downarrow\\rangle$\")\n",
    "ax[1].plot(corrected_rabi_durations*1e-3, data_prep.mean(2).mean(0)[:,1], label = r\"P$_{|\\downarrow\\rangle}$, prepare $|\\downarrow\\rangle$\")\n",
    "ax[1].set_xlabel('Effective Rabi time (µs)')\n",
    "ax[1].legend()\n",
    "\n",
    "data_singleshot=(data.sum(2)[:,:,0]-data.sum(2)[:,:,1])>0\n",
    "\n",
    "def rabi_fit(t,f,a,b):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)))\n",
    "\n",
    "try:\n",
    "    guess=[0.015,-0.5,1]\n",
    "    popt,popc=sp.curve_fit(rabi_fit,corrected_rabi_durations/1e3,data_singleshot.mean(0),guess)\n",
    "    label_rabi=r\"Fit, $\\tau_{\\pi}$ = %.2f µs\" %(1/popt[0]/2)\n",
    "    x_fine = 1e-3*np.linspace(corrected_rabi_durations[0],corrected_rabi_durations[-1],501)\n",
    "    ax[2].plot(x_fine,rabi_fit(x_fine,*popt),'-',label=label_rabi)\n",
    "except Exception as e:\n",
    "    print('fit failed: ', e) \n",
    "\n",
    "ax[2].plot(corrected_rabi_durations*1e-3, data_singleshot.mean(0), \"o\",label = \"data\")\n",
    "ax[2].set_xlabel('Effective Rabi time (µs)')\n",
    "ax[2].set_ylabel('Nuclear spin probability')\n",
    "ax[2].grid()\n",
    "ax[2].set_ylim([0,1])\n",
    "\n",
    "ax[2].legend()\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0a36fae-4ad1-4b3e-b997-02e7451e3a0c",
   "metadata": {},
   "source": [
    "## Sideband rabi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7a2a417-0416-4449-ab54-c119f916af5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3\\\\Nuclear_Rabi_duration_flattop\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = ['20231009225439_\\\\', '20231010030142_\\\\']\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    directory=os.listdir(path+timestamp)[0]\n",
    "    filename=getfiles_hdf5(path+timestamp+directory)\n",
    "    data=load_h5_to_dic(path+timestamp+directory+r'\\\\'+filename[0])\n",
    "    data_list.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1ce7651-1e72-4591-bfc1-44bba6daf567",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "Key_field = '0'\n",
    "data0=data_list[0][0][Key_field]['click_array']\n",
    "data1=data_list[1][0][Key_field]['click_array']\n",
    "rabi_durations = data_list[0][0][Key_field]['rabi_durations']\n",
    "corrected_rabi_durations = rabi_durations + adiabatic_spin_sigma*(adiabatic_spin_Nsigma) # Effective pulse duration including sigmoids\n",
    "\n",
    "x_fine = 1e-3*np.linspace(corrected_rabi_durations[0],corrected_rabi_durations[-1],501)\n",
    "\n",
    "fig,ax=plt.subplots(1,1, figsize=(11, 5), tight_layout=True)\n",
    "\n",
    "data_singleshot0=(data0.sum(2)[:,:,0]-data0.sum(2)[:,:,1])>0\n",
    "data_singleshot1=(data1.sum(2)[:,:,0]-data1.sum(2)[:,:,1])>0\n",
    "\n",
    "def rabi_fit(t,f,a,b):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)))\n",
    "\n",
    "try:\n",
    "    guess=[0.015,-0.5,1]\n",
    "    popt,popc=sp.curve_fit(rabi_fit,corrected_rabi_durations/1e3, data_singleshot0.mean(0),guess)\n",
    "    label_rabi=r\"$\\tau_{\\pi}$ = %.1f µs | d$_{ft}$ =  = %.1f µs\" %(1/popt[0]/2, 1/popt[0]/2-adiabatic_spin_sigma*(adiabatic_spin_Nsigma)*1e-3)\n",
    "    ax.plot(x_fine, rabi_fit(x_fine,*popt), 'b-', alpha=0.5, label=label_rabi)\n",
    "except Exception as e:\n",
    "    print('fit failed: ', e) \n",
    "    \n",
    "try:\n",
    "    guess=[0.015,-0.5,1]\n",
    "    popt,popc=sp.curve_fit(rabi_fit,corrected_rabi_durations/1e3, data_singleshot1.mean(0),guess)\n",
    "    label_rabi=r\"$\\tau_{\\pi}$ = %.1f µs | d$_{ft}$ =  = %.1f µs\" %(1/popt[0]/2, 1/popt[0]/2-adiabatic_spin_sigma*(adiabatic_spin_Nsigma)*1e-3)\n",
    "    ax.plot(x_fine, rabi_fit(x_fine,*popt), 'r-', alpha=0.5, label=label_rabi)\n",
    "except Exception as e:\n",
    "    print('fit failed: ', e) \n",
    "\n",
    "ax.plot(corrected_rabi_durations*1e-3, data_singleshot0.mean(0), \"bo\", label = \"Blue Sideband Pulse\")\n",
    "ax.plot(corrected_rabi_durations*1e-3, data_singleshot1.mean(0), \"ro\", label = \"Red Sideband Pulse\")\n",
    "ax.set_xlabel('Effective Rabi time (µs)')\n",
    "ax.set_ylabel('Nuclear spin probability')\n",
    "ax.grid()\n",
    "ax.set_ylim([0,1])\n",
    "\n",
    "ax.legend()\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60af9d3e-ae22-46b6-a911-19c9eef23ffc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-10T12:38:08.457277Z",
     "iopub.status.busy": "2023-10-10T12:38:08.457277Z",
     "iopub.status.idle": "2023-10-10T12:38:08.477289Z",
     "shell.execute_reply": "2023-10-10T12:38:08.477289Z",
     "shell.execute_reply.started": "2023-10-10T12:38:08.457277Z"
    }
   },
   "source": [
    "## Composite pulse rabi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdfe8200-a8ee-49df-ae1c-d6d47ee551be",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3\\\\Nuclear_Rabi_5pulse\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = ['20231011094811_\\\\']\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    directory=os.listdir(path+timestamp)[0]\n",
    "    filename=getfiles_hdf5(path+timestamp+directory)\n",
    "    data=load_h5_to_dic(path+timestamp+directory+r'\\\\'+filename[0])\n",
    "    data_list.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cd4c2d0-1d47-4955-a511-b44a68586a2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "Key_field = '0'\n",
    "data=data_list[0][0][Key_field]['click_array']\n",
    "rabi_durations = data_list[0][0][Key_field]['rabi_durations']\n",
    "corrected_rabi_durations = rabi_durations + adiabatic_spin_sigma*(adiabatic_spin_Nsigma) # Effective pulse duration including sigmoids\n",
    "\n",
    "\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(2,1)\n",
    "corrected_rabi_durations = adiabatic_spin_sigma*(adiabatic_spin_Nsigma)+rabi_durations # Effective pulse duration including sigmoids\n",
    "\n",
    "\n",
    "hist=ax[0].hist(np.concatenate(data.sum(2)[:,:,0]-data.sum(2)[:,:,1]), bins=np.arange(-40,40,2),alpha=0.5, label='rabi')\n",
    "ax[0].vlines(0,0,max(hist[0]))\n",
    "ax[0].set_xlabel('delta clicks during NuclearSpin RO')\n",
    "ax[0].set_ylabel('counts')\n",
    "ax[0].legend()\n",
    "\n",
    "ax[1].plot(corrected_rabi_durations*1e-3, data.mean(2).mean(0)[:,0], label = r\"${|\\uparrow\\rangle}$\")\n",
    "ax[1].plot(corrected_rabi_durations*1e-3, data.mean(2).mean(0)[:,1], label = r\"${|\\downarrow\\rangle}$\")\n",
    "ax[1].set_ylabel(\"Mean counts\")\n",
    "ax[1].set_xlabel('Rabi duration (µs)')\n",
    "ax[1].legend()\n",
    "\n",
    "fig,ax=plt.subplots(1,1,figsize=(12,6))\n",
    "\n",
    "def rabi_fit(t,f,a,b):\n",
    "    return a*(b-np.cos(2*np.pi*f*t))\n",
    "\n",
    "try:\n",
    "    guess=[0.016,0.4,1]\n",
    "    popt,popc=sp.curve_fit(rabi_fit,corrected_rabi_durations*1e-3,data_singleshot.mean(0),guess)\n",
    "    label_rabi=r\"$\\tau_{\\pi}$ = %.1f µs | d$_{ft}$ =  = %.1f µs\" %(1/popt[0]/2, 1/popt[0]/2-adiabatic_spin_sigma*(adiabatic_spin_Nsigma)*1e-3)\n",
    "    x_fine = 1e-3*np.linspace(corrected_rabi_durations[0],corrected_rabi_durations[-1],501)\n",
    "    ax.plot(x_fine,rabi_fit(x_fine,*popt),'b-', alpha=0.5, label=label_rabi)\n",
    "except Exception as e:\n",
    "    print('fit failed: ', e) \n",
    "\n",
    "threshold = 0\n",
    "data_singleshot = (data.sum(2)[:,:,0]-data.sum(2)[:,:,1]) > threshold\n",
    "ax.plot(corrected_rabi_durations*1e-3, data_singleshot.mean(0), 'bo')\n",
    "ax.set_xlabel('Rabi duration (µs)')\n",
    "ax.set_ylabel(r'P$_{|\\uparrow\\rangle}$')\n",
    "ax.set_ylim([0,1])\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e9cc35b-98c2-4a82-896e-3e3f0f17d22d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-01T13:17:17.695925Z",
     "iopub.status.busy": "2023-10-01T13:17:17.695925Z",
     "iopub.status.idle": "2023-10-01T13:17:17.715928Z",
     "shell.execute_reply": "2023-10-01T13:17:17.715928Z",
     "shell.execute_reply.started": "2023-10-01T13:17:17.695925Z"
    }
   },
   "source": [
    "## Frequency sweep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96301014-c4f4-425f-90c4-ea361cd83e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "7750e6+0.493e6+0.245e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24bf07de-1ede-4cad-80f1-b6b69afd0e73",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3\\\\Nuclear_Rabi_frequency_amplitude_flattop\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = ['20230930210016_\\\\']\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    directory=os.listdir(path+timestamp)[0]\n",
    "    filename=getfiles_hdf5(path+timestamp+directory)\n",
    "    data=load_h5_to_dic(path+timestamp+directory+r'\\\\'+filename[0])\n",
    "    data_list.append(data)\n",
    "\n",
    "def lorentz(delta,delta0,kappa,a,b):\n",
    "    return a/(1+(delta-delta0)**2/(kappa/2)**2)+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54184e31-9643-43fa-841b-f103a6fd2bbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "Key_field = '0'\n",
    "data=data_list[0][0][Key_field]['click_array']\n",
    "data_prep=data_list[0][0][Key_field]['click_preparation_array']\n",
    "freqs = data_list[0][0][Key_field]['freqs']\n",
    "rabi_amps = data_list[0][0][Key_field]['rabi_amp']\n",
    "\n",
    "centre_freq = 7.5\n",
    "\n",
    "fig,ax=plt.subplots(1,1,figsize=(10,5))\n",
    "print(directory)\n",
    "plot_amps = []\n",
    "central_frequencies = []\n",
    "\n",
    "for i in range(len(rabi_amps)-1):\n",
    "\n",
    "    data_i = data[:,:,i]\n",
    "    data_prep_i = data_prep[:,:,i]\n",
    "\n",
    "    \n",
    "    data_singleshot=(data_i.sum(2)[:,:,0]-data_i.sum(2)[:,:,1])>0\n",
    "    x = freqs*1e-3-Photon_IF*1e-3-centre_freq\n",
    "    y = data_singleshot.mean(0)\n",
    "    \n",
    "    try:\n",
    "        guess = [x[np.argmax(y)], (x[-1]-x[0])/5, max(y)-min(y), min(y)]\n",
    "        popt, pcov = sp.curve_fit(lorentz,x,y,guess,maxfev = 10000)\n",
    "        print(popt)\n",
    "        x_fit = np.linspace(x[0],x[-1],201)\n",
    "        y_fit = lorentz(x_fit,*popt)\n",
    "        \n",
    "        plt.plot(x_fit,y_fit,color=colors[i], alpha=0.5)\n",
    "        #y_fit = lorentz(x_fit,*guess)\n",
    "        #plt.plot(x_fit,y_fit, label = 'fit')\n",
    "        maxfreq = popt[0]\n",
    "    except Exception as e:\n",
    "        print('fit failed : ', e)\n",
    "        popt=guess\n",
    "    \n",
    "    plot_amps.append(rabi_amps[i])\n",
    "    central_frequencies.append(popt[0])\n",
    "    \n",
    "    ax.plot(x, y, \"o-\", color=colors[i], label = \"amp = %.2f | freq = %.1f\"%(rabi_amps[i], popt[0]))\n",
    "    ax.set_xlabel('Sideband Frequency (MHz)')\n",
    "    ax.set_ylabel('Nuclear spin probability')\n",
    "    ax.grid()\n",
    "    ax.set_ylim([0,1])\n",
    "    ax.legend()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(np.array(plot_amps)**2, central_frequencies, 'o-')\n",
    "plt.grid()\n",
    "plt.ylabel('Sideband Frequency (kHz)')\n",
    "plt.xlabel('Relative amplitude ^ 2 (a.u.)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acc65b9e-5544-4a83-8bd3-7a5f4c5db46b",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.array(plot_amps)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "962af101-b3a8-4e5e-969f-bd59b16dc1bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "m, n = np.polyfit(np.array(plot_amps)**2, central_frequencies, 1)\n",
    "print(m*0.4**2 + n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a7fd19b-e68b-4a5d-9aae-249f7e29bcb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "interpulse_delay = (4*echo_time + 4*switch_duration_extra + 1e5) * 1e-6  # s\n",
    "\n",
    "delta_phi = 2*np.pi * central_frequencies / interpulse_delay                        # s-1\n",
    "\n",
    "kappa = 4.41e6            # s-1\n",
    "delta = 780e3 * 2 * np.pi # s-1\n",
    "T1 = 1.1e-3               # s\n",
    "\n",
    "OmegaR_05 = 50e3 * 2 * np.pi # Rabi frequency at 0.05 relative power. See notebook AC Stark shift\n",
    "\n",
    "g_0 = np.sqrt(kappa / T1) / 2            # s-1\n",
    "R = kappa * OmegaR_05 / (2 * g_0 * 0.05) # s-1\n",
    "epsilon = R * ac_amps                    \n",
    "\n",
    "chi = 2 * g_0 ** 2 / delta                # s-1\n",
    "nbar = epsilon**2 / (delta**2 + kappa**2/4) # photons\n",
    "\n",
    "vals =np.polyfit(nbar, delta_phi, 1)\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.plot(nbar, delta_phi, 'o')\n",
    "plt.plot(nbar, np.polyval(vals, nbar))\n",
    "#plt.ylim([-0.2, 0.05])\n",
    "plt.grid()\n",
    "plt.title(r\"$\\chi_{fit}/2\\pi$ = %d Hz  |  $\\chi/2\\pi$ = $2g_0^2/2\\pi \\delta$= %d Hz\"%(vals[0]*1e3, chi/2/np.pi))\n",
    "plt.xlabel(r\"$\\bar{n}$\")\n",
    "plt.ylabel(r\"$\\Delta \\Phi / t$ (kHz)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0bacae5-bcd5-491c-8507-e18e24f913a1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63559e31-41a0-4cde-9e8c-f31e11e86b1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3\\\\Nuclear_Rabi_frequency_amplitude_flattop\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = ['20231001155126_\\\\']\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    directory=os.listdir(path+timestamp)[0]\n",
    "    filename=getfiles_hdf5(path+timestamp+directory)\n",
    "    data=load_h5_to_dic(path+timestamp+directory+r'\\\\'+filename[0])\n",
    "    data_list.append(data)\n",
    "\n",
    "def lorentz(delta,delta0,kappa,a,b):\n",
    "    return a/(1+(delta-delta0)**2/(kappa/2)**2)+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "457c4902-e5b3-450f-8aa8-64d23e708907",
   "metadata": {},
   "outputs": [],
   "source": [
    "Key_field = '0'\n",
    "data=data_list[0][0][Key_field]['click_array']\n",
    "data_prep=data_list[0][0][Key_field]['click_preparation_array']\n",
    "freqs = data_list[0][0][Key_field]['freqs']\n",
    "rabi_amps = data_list[0][0][Key_field]['rabi_amp']\n",
    "\n",
    "centre_freq = 7.5\n",
    "\n",
    "\n",
    "fig,ax=plt.subplots(1,1,figsize=(10,5))\n",
    "print(directory)\n",
    "plot_amps = []\n",
    "central_frequencies = []\n",
    "\n",
    "\n",
    "for i in range(len(rabi_amps)):\n",
    "\n",
    "    data_i = data[:,:,i]\n",
    "    data_prep_i = data_prep[:,:,i]\n",
    "\n",
    "    \n",
    "    data_singleshot=(data_i.sum(2)[:,:,0]-data_i.sum(2)[:,:,1])>0\n",
    "    x = freqs*1e-3-Photon_IF*1e-3-centre_freq\n",
    "    y = data_singleshot.mean(0)\n",
    "    \n",
    "    try:\n",
    "        guess = [x[np.argmin(y)], (x[-1]-x[0])/5, min(y)-max(y), max(y)]\n",
    "        popt, pcov = sp.curve_fit(lorentz,x,y,guess,maxfev = 10000)\n",
    "        print(popt)\n",
    "        x_fit = np.linspace(x[0],x[-1],201)\n",
    "        y_fit = lorentz(x_fit,*popt)\n",
    "        \n",
    "        plt.plot(x_fit,y_fit,color=colors[i], alpha=0.5)\n",
    "        #y_fit = lorentz(x_fit,*guess)\n",
    "        #plt.plot(x_fit,y_fit, label = 'fit')\n",
    "        maxfreq = popt[0]\n",
    "    except Exception as e:\n",
    "        print('fit failed : ', e)\n",
    "        popt=guess\n",
    "    \n",
    "    plot_amps.append(rabi_amps[i])\n",
    "    central_frequencies.append(popt[0])\n",
    "    \n",
    "    ax.plot(x, y, \"o-\", color=colors[i], label = \"amp = %.2f\"%(rabi_amps[i]))\n",
    "    ax.set_xlabel('Sideband Frequency (kHz)')\n",
    "    ax.set_ylabel('Nuclear spin probability')\n",
    "    ax.grid()\n",
    "    ax.set_ylim([0,1])\n",
    "    ax.legend()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(np.array(plot_amps)**2, central_frequencies, 'o-')\n",
    "plt.grid()\n",
    "plt.ylabel('Sideband Frequency (kHz)')\n",
    "plt.xlabel('Relative amplitude ^ 2 (a.u.)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df4b0c36-69e3-482e-9157-3dc0ffc67008",
   "metadata": {},
   "outputs": [],
   "source": [
    "m, n = np.polyfit(np.array(plot_amps)**2, central_frequencies, 1)\n",
    "print(m*0.5**2 + n)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "774cc7d1-792c-4b7b-90de-cf7817d8cb4c",
   "metadata": {},
   "source": [
    "## Pulse fourier transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3f364a0-7c0e-4417-afc5-49f293a0819d",
   "metadata": {},
   "outputs": [],
   "source": [
    "adiabatic_spin_Nsigma * adiabatic_spin_sigma * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b9f4f82-8e96-4173-9f92-f0a809b599db",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "fig, (ax1, ax2) = plt.subplots(1,2,figsize = (15,5))\n",
    "pulse_length = 20000 # in ns\n",
    "adiabatic_spin_sigma = 1000\n",
    "zero_padding = pulse_length//2 * 5\n",
    "\n",
    "tau = 4e-6*np.arange(pulse_length//4 + zero_padding*2 + adiabatic_spin_Nsigma * adiabatic_spin_sigma)\n",
    "y = np.zeros(zero_padding)\n",
    "y = np.append(y, np.ones(pulse_length//4 + adiabatic_spin_Nsigma * adiabatic_spin_sigma)*adiabatic_spin_amp)\n",
    "y = np.append(y, np.zeros(zero_padding))\n",
    "\n",
    "y = y /max(y)\n",
    "\n",
    "freq = np.fft.fftfreq(len(tau),d=(tau[1]-tau[0]))\n",
    "freq = np.fft.fftshift(freq)\n",
    "yft = np.fft.fftshift(abs(np.fft.fft(y)))\n",
    "\n",
    "ax1.plot(tau,y,label=r'Square')\n",
    "ax1.set(xlabel = 'Time (ms)',ylabel = 'Amplitude')\n",
    "\n",
    "\n",
    "n_mask = 100\n",
    "ax2.plot(freq[len(freq)//2-n_mask:len(freq)//2+n_mask],yft[len(freq)//2-n_mask:len(freq)//2+n_mask],'-o',label=r'Square')\n",
    "ax2.set(xlabel = 'freq (kHz)',ylabel = 'FFT')\n",
    "ax2.set_yscale(\"log\")\n",
    "    \n",
    "for i in np.arange(2, 7, 2):\n",
    "    adiabatic_spin_Nsigma = i\n",
    "    \n",
    "    tau = 4e-6*np.arange(pulse_length//4 + zero_padding*2 + adiabatic_spin_Nsigma * adiabatic_spin_sigma * 2)\n",
    "    \n",
    "    y = np.zeros(zero_padding)\n",
    "    y = np.append(y, ErfRising(adiabatic_spin_Nsigma * adiabatic_spin_sigma, adiabatic_spin_sigma, adiabatic_spin_amp)/ErfRising(adiabatic_spin_Nsigma * adiabatic_spin_sigma, adiabatic_spin_sigma, adiabatic_spin_amp)[-1])\n",
    "    y = np.append(y, np.ones(pulse_length//4))\n",
    "    y = np.append(y, 1-ErfRising(adiabatic_spin_Nsigma * adiabatic_spin_sigma, adiabatic_spin_sigma, adiabatic_spin_amp)/ErfRising(adiabatic_spin_Nsigma * adiabatic_spin_sigma, adiabatic_spin_sigma, adiabatic_spin_amp)[-1])\n",
    "    y = np.append(y, np.zeros(zero_padding))\n",
    "    \n",
    "    #y = y /max(y)\n",
    "    freq = np.fft.fftfreq(len(tau),d=(tau[1]-tau[0]))\n",
    "    freq = np.fft.fftshift(freq)\n",
    "    yft = np.fft.fftshift(abs(np.fft.fft(y)))\n",
    "\n",
    "    ax1.plot(tau,y,label=r'$\\sigma$ = %d'%i)\n",
    "    ax1.set(xlabel = 'Time (ms)',ylabel = 'Amplitude')\n",
    "    \n",
    "    \n",
    "    n_mask = 2000\n",
    "    ax2.plot(freq[len(freq)//2-n_mask:len(freq)//2+n_mask],yft[len(freq)//2-n_mask:len(freq)//2+n_mask],'-',label=r'$\\sigma$ = %d'%i)\n",
    "    ax2.set(xlabel = 'freq (kHz)',ylabel = 'FFT')\n",
    "    ax2.set_yscale(\"log\")\n",
    "\n",
    "ax1.legend()\n",
    "ax2.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "729cb60a-5d0b-4015-a597-c6abfbf507b0",
   "metadata": {},
   "source": [
    "## Raman Rabi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4c39975-4316-432c-87e7-cb8c56abf7d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3\\\\sideband_ramsey\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = ['20231106100237_\\\\',\n",
    "                  '20231106101557_\\\\',\n",
    "                  '20231106102605_\\\\',\n",
    "                  '20231106103613_\\\\',\n",
    "                  '20231106104622_\\\\',\n",
    "                  '20231106105630_\\\\',\n",
    "                  '20231106110638_\\\\',\n",
    "                  \"20231106111646_\\\\\",\n",
    "                  \"20231106112654_\\\\\",\n",
    "                  \"20231106113702_\\\\\",\n",
    "                 ]\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    directory=os.listdir(path+timestamp)[0]\n",
    "    filename=getfiles_hdf5(path+timestamp+directory)\n",
    "    data=load_h5_to_dic(path+timestamp+directory+r'\\\\'+filename[0])\n",
    "    data_list.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aeabf46a-d587-410a-85fb-a5b31e823a44",
   "metadata": {},
   "outputs": [],
   "source": [
    "data0=data_list[0][0][Key_field]['click_array']\n",
    "data1=data_list[1][0][Key_field]['click_array']\n",
    "rabi_durations = data_list[0][0][Key_field]['rabi_durations']\n",
    "corrected_rabi_durations = rabi_durations + adiabatic_spin_sigma*(adiabatic_spin_Nsigma) # Effective pulse duration including sigmoids\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a192842-b68b-45fd-b042-573de68f4c90",
   "metadata": {},
   "outputs": [],
   "source": [
    "rabi0 = []\n",
    "rabi1 = []\n",
    "rabi2 = []\n",
    "rabi3 = []\n",
    "\n",
    "rabi_amps = np.linspace(0,0.2,10)\n",
    "\n",
    "for n in range(len(data_list)):\n",
    "    data = data_list[n][0]['0']['click_array']\n",
    "    \n",
    "    rabi0.append(data.mean(2).mean(0)[:,0][2])\n",
    "    rabi1.append(data.mean(2).mean(0)[:,1][2])\n",
    "    rabi2.append(data.mean(2).mean(0)[:,2][2])\n",
    "    rabi3.append(data.mean(2).mean(0)[:,3][2])\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c78dd7c1-b7e3-4f76-8656-6d5727f1689f",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "len(rabi0)\n",
    "\n",
    "\n",
    "plt.plot(rabi_amps[:len(rabi0)],rabi0)\n",
    "plt.plot(rabi_amps[:len(rabi0)],rabi1)\n",
    "plt.plot(rabi_amps[:len(rabi0)],rabi2)\n",
    "plt.plot(rabi_amps[:len(rabi0)],rabi3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d393abc5-4c56-42ac-8e0d-68568fdce892",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.clf()\n",
    "plt.plot(data.mean(2).mean(0)[:,0])\n",
    "plt.plot(data.mean(2).mean(0)[:,1])\n",
    "plt.plot(data.mean(2).mean(0)[:,2])\n",
    "plt.plot(data.mean(2).mean(0)[:,3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dad2f5b-a316-4832-bbc3-96470e08bc6c",
   "metadata": {},
   "source": [
    "## Rabi freq vs Nuclear spin state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a13c59e0-cde0-46da-af9d-1f76cf9d0771",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"Z:\\\\SMPD3-8\\\\SpinRun3\\\\raman_rabi\\\\\"\n",
    "timestamp_list = [\"20231210225528_\\\\\",\n",
    "                  \"20231211035555_\\\\\"]\n",
    "\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    directory=os.listdir(path+timestamp)[0]\n",
    "    filename=getfiles_hdf5(path+timestamp+directory)\n",
    "    data=load_h5_to_dic(path+timestamp+directory+r'\\\\'+filename[0])\n",
    "    data_list.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f48c2140-ca00-47ce-b87b-e8e16685a134",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_list[1][0][Key_field].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1791ea23-9ee8-4b8d-afd2-66351c6bf7bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "Key_field = '0'\n",
    "raman_pulse_durations = np.linspace(4,400,100)\n",
    "\n",
    "data_clicks_list,data_prep_clicks_list = [],[]\n",
    "\n",
    "for i in range(2):\n",
    "    data_clicks_list.append(data_list[i][0][Key_field]['click_array'])\n",
    "    #data_prep_clicks_list.append(data_list[i][0][Key_field]['click_array_prep'])\n",
    "\n",
    "readout_freqs = data_list[0][0][Key_field]['readout_freqs']\n",
    "raman_pulse_durations = data_list[1][0][Key_field]['raman_pulse_durations']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b12e1ab0-7d53-48fc-9337-1374d544f972",
   "metadata": {},
   "outputs": [],
   "source": [
    "raman_pulse_durations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60f212a7-15fe-4aee-b464-ce7f9effe02b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_index= 0\n",
    "\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "threshold = 80\n",
    "p_data = (data_clicks_list[data_index][:,:,3-data_index]>threshold)\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft( p_data.mean(0)- p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "sigmoid_length = 5\n",
    "\n",
    "############################### Fit #################################\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data_clicks_list[data_index], frequency_domain = True, accumulated=True)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "def rabi_fit(t,f,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi))\n",
    "\n",
    "guess = [1e-3*rabi_freq,0.5,1,1*np.pi]\n",
    "\n",
    "try:est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "T_half_period = 0.5/est[0]\n",
    "T_min = 0.5/(est[0]+std[0])\n",
    "T_max = 0.5/(est[0]-std[0])\n",
    "T_err = T_half_period-T_min\n",
    "\n",
    "\n",
    "# T_pi = fine[np.argmin(data_fit[:int(len(fine)*T_half_period*2/max(fine))])]\n",
    "# T_pio2 = T_pi-T_half_period/2\n",
    "T_pi_list=(-est[3]+np.arange(-5,5)*np.pi)/(2*np.pi*est[0])\n",
    "T_pi_list_p=T_pi_list[T_pi_list>0]\n",
    "#T_pi = T_pi_list_p[np.argmin(T_pi_list_p)]\n",
    "#T_pio2 = T_pi_list_p[np.argmin(T_pi_list_p)]-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data_clicks_list[data_index]).mean(0)[:,i] , (data_clicks_list[data_index]).std(0)[:,i]/np.sqrt(len(data_clicks_list[data_index])), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([0,None])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)): ax[1].plot(x,pops[i], label = labels[i])\n",
    "else:\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data_clicks_list[data_index]))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "ax[1].plot(fine,data_fit)\n",
    "ax[1].set_ylim(0,1)\n",
    "#ax[1].set_xlim(0,200)\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "\n",
    "ax[2].plot(fft_x,fft_y,\"o-\")\n",
    "ax[2].set_ylim(0,None)\n",
    "ax[2].set_xlim(0,None)\n",
    "ax[2].axvline(rabi_freq,linestyle = \"--\", color = \"black\", alpha = 0.5)\n",
    "ax[2].set_xlabel(\"Rabi frequency (Hz)\")\n",
    "ax[2].set_ylabel(\"Population\")\n",
    "ax[2].set_title(\"Max FFT freq = %i Hz\"%rabi_freq, fontsize = \"small\")\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "# plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed025279-6e0a-4646-bad9-60adf1f7d166",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rabi_decay_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "labels = [r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "plt.figure(figsize = (8.5,5))\n",
    "counter=0\n",
    "for data_index in [3,2]:\n",
    "    \n",
    "    p_data = (data_clicks_list[counter][:,:,data_index]>threshold)\n",
    "    x = 4e-6*np.array(raman_pulse_durations)\n",
    "    fft_x = 1e3*np.fft.rfftfreq(len(x),x[1]-x[0])\n",
    "    fft_y = np.abs(np.fft.rfft( p_data.mean(0)- p_data.mean(0).mean()))\n",
    "    rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    sigmoid_length = 5\n",
    "    \n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data_clicks_list[counter], frequency_domain = True, accumulated=True)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "    y = pops[data_index]\n",
    "    guess = [1e-3*rabi_freq, 1e2, 0.5,1,1*np.pi]\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_decay_fit, x,  y)\n",
    "    \n",
    "    plt.plot(x, y, 'o', color=colors[counter])\n",
    "    plt.plot(fine, data_fit, color=colors[counter], label = f'{labels[data_index]}  $\\Omega_R$ = {est[0]*1e3:.1f} Hz')\n",
    "    counter+=1\n",
    "\n",
    "plt.legend()\n",
    "plt.ylim(0,None)\n",
    "plt.xlim(0,None)\n",
    "plt.xlabel(\"Rabi Pulse duration (ms)\")\n",
    "plt.ylabel(\"Population\")\n",
    "plt.tight_layout()\n",
    "#plt.savefig(\"Z:\\\\SMPD3-8\\\\SpinRun3\\\\raman_rabi\\\\20231209052436_\\\\20231209052436_raman_rabi\\\\Rabi_frequency_comparison.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22d3df89-c129-41de-aa0a-a2647ac06ea5",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "labels = [r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "plt.figure(figsize = (8.5,5))\n",
    "\n",
    "for data_index in range(2):\n",
    "    p_data = (data_clicks_list[data_index][:,:,-2+data_index]>threshold)\n",
    "    x = np.array(raman_pulse_durations)\n",
    "    fft_x = 1e3*np.fft.rfftfreq(len(x),x[1]-x[0])\n",
    "    fft_y = np.abs(np.fft.rfft( p_data.mean(0)- p_data.mean(0).mean()))\n",
    "    rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    sigmoid_length = 5\n",
    "    \n",
    "    \n",
    "    plt.plot(fft_x,fft_y,\"o-\", label = labels[data_index]+\", Peak = %i Hz\"%rabi_freq)\n",
    "    plt.axvline(rabi_freq,linestyle = \"--\", color = \"black\", alpha = 0.5)\n",
    "\n",
    "plt.legend()\n",
    "plt.ylim(0,None)\n",
    "plt.xlim(0,None)\n",
    "plt.xlabel(\"Rabi frequency (Hz)\")\n",
    "plt.ylabel(\"FFT (Population)\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Z:\\\\SMPD3-8\\\\SpinRun3\\\\raman_rabi\\\\20231209052436_\\\\20231209052436_raman_rabi\\\\Rabi_frequency_comparison.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26877390-f79e-4ab2-8f6f-b4b85d9d0ed0",
   "metadata": {},
   "source": [
    "# Nuclear Ramsey "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae1ca910-08fb-41d3-a0ec-01d3b16f2eb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3\\\\Nuclear_Ramsey_sequence_refocusing\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = ['20230926203540_\\\\']\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    directory=os.listdir(path+timestamp)[0]\n",
    "    filename=getfiles_hdf5(path+timestamp+directory)\n",
    "    data=load_h5_to_dic(path+timestamp+directory+r'\\\\'+filename[0])\n",
    "    data_list.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26955d49-e846-45dc-b9c2-47fc99f73d90",
   "metadata": {},
   "outputs": [],
   "source": [
    "ramsey_detuning*1e3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06b331a4-ddcf-4c97-9f0b-0b572d3d1379",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "Key_field = '0'\n",
    "times_ramsey=data_list[0][0][Key_field]['times_ramsey']\n",
    "data=data_list[0][0][Key_field]['click_array']\n",
    "ramsey_detuning=data_list[0][0][Key_field]['ramsey_detuning']\n",
    "excess = data.mean(2).mean(0)\n",
    "average_number = data.shape[0]\n",
    "\n",
    "def ramsey_osc(t,T,f,alpha,beta,phi,a):\n",
    "    return alpha/2*np.exp(-(t/T))*np.cos(2*np.pi*f*t+2*np.pi*phi) + beta + a*t\n",
    "\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(3,1, figsize=(10, 10))\n",
    "\n",
    "ax[0].plot(times_ramsey*1e-6, data.mean(2).mean(0)[:,0]     , label = r\"${|\\uparrow\\rangle}$\")\n",
    "ax[0].plot(times_ramsey*1e-6, data.mean(2).mean(0)[:,1]     , label = r\"${|\\downarrow\\rangle}$\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Ramsey inter pulse delay (ms)')\n",
    "ax[0].legend()\n",
    "\n",
    "data_singleshot=(data.sum(2)[:,:,0]-data.sum(2)[:,:,1])>0\n",
    "\n",
    "guess=[1e3, 2e-3, 2,  0.2, 0, 0]\n",
    "try:\n",
    "    popt, pcov = sp.curve_fit(ramsey_osc, times_ramsey*1e-6, data_singleshot.mean(0), guess)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "    popt = guess\n",
    "    \n",
    "times_ramsey_fit=np.linspace(times_ramsey[0],times_ramsey[-1],201)\n",
    "linear_background = popt[5] * times_ramsey*1e-3 + popt[3]\n",
    "linear_background_fit = popt[5] * times_ramsey_fit*1e-3 + popt[3]\n",
    "\n",
    "ax[1].plot(times_ramsey*1e-6, data_singleshot.mean(0))\n",
    "ax[1].plot(times_ramsey_fit*1e-6, ramsey_osc(times_ramsey_fit*1e-6, *popt), label=r\"$T_2^*$ = %.2f s | f = %.3f Hz\"%(popt[0]*1e-3, popt[1]*1e3))\n",
    "ax[1].set_xlabel('Ramsey inter pulse delay (ms)')\n",
    "ax[1].set_ylabel(r'P$_{|\\uparrow\\rangle}$')\n",
    "ax[1].legend()\n",
    "\n",
    "############# FFT #############\n",
    "x = times_ramsey*1e-3\n",
    "y = data_singleshot.mean(0)-0.5\n",
    "\n",
    "x_freq=np.fft.fftfreq(len(x),d=(x[1]-x[0]))\n",
    "y_fft = np.abs(np.fft.rfft(y))\n",
    "\n",
    "ax[2].plot(1e3*x_freq[:len(x_freq)//2],y_fft[:len(x_freq)//2])\n",
    "ax[2].set_xlim(0,None)\n",
    "ax[2].set_xlabel(\"frequency (kHz)\")\n",
    "ax[2].set_ylabel(r\"FFT (P$_{|\\uparrow\\rangle}$)\")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7221c829-6411-407a-ac52-986ed05541c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from scipy.signal import savgol_filter\n",
    "Key_field = '0'\n",
    "times_ramsey=data_list[0][0][Key_field]['times_ramsey']\n",
    "data        =data_list[0][0][Key_field]['click_array']\n",
    "\n",
    "window = 5\n",
    "order = 2\n",
    "\n",
    "ramsey_detuning=data_list[0][0][Key_field]['ramsey_detuning']\n",
    "excess = data.mean(2).mean(0)\n",
    "\n",
    "average_number = data.shape[0]\n",
    "\n",
    "def ramsey_osc(t,T,f,alpha,beta,phi,a):\n",
    "    return alpha/2*np.exp(-(t/T))*np.cos(2*np.pi*f*t+2*np.pi*phi) + beta + a*t\n",
    "\n",
    "\n",
    "slices = 15\n",
    "slice_size = data.shape[0]//slices\n",
    "real_measurement_time = np.linspace(0,13.16,slices)+56/60/2\n",
    "\n",
    "frequency_list = []\n",
    "t2_list = []\n",
    "contrast_list = []\n",
    "for i in range(slices):\n",
    "    \n",
    "    data_sliced = data[(i*slice_size):(((i+1)*slice_size)%data.shape[0])]\n",
    "    ############################### Plotting ###############################\n",
    "\n",
    "    data_singleshot=(data_sliced.sum(2)[:,:,0]-data_sliced.sum(2)[:,:,1])>0\n",
    "    \n",
    "    up_state_prob = savgol_filter(data_singleshot.mean(0), window, order)\n",
    "    time_xaxis    = savgol_filter(times_ramsey*1e-6      , window, order)\n",
    "    \n",
    "    guess=[1e3, 2e-3, 2,  0.2, 0, 0]\n",
    "    try:\n",
    "        popt, pcov = sp.curve_fit(ramsey_osc, time_xaxis, up_state_prob, guess)\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        popt = guess\n",
    "\n",
    "    times_xaxis_fit=np.linspace(time_xaxis[0],time_xaxis[-1],201)\n",
    "    linear_background = popt[5] * time_xaxis + popt[3]\n",
    "    linear_background_fit = popt[5] * time_xaxis + popt[3]\n",
    "    \n",
    "    contrast_list.append(popt[2])\n",
    "    t2_list.append(popt[0])\n",
    "    frequency_list.append(popt[1])\n",
    "    \n",
    "    if True and i%10== 0:\n",
    "        fig,ax=plt.subplots(1,1, figsize=(10, 3))\n",
    "        ax.set_title('Slice number: %d'%i)\n",
    "        ax.plot(time_xaxis, up_state_prob)\n",
    "        ax.plot(times_xaxis_fit, ramsey_osc(times_xaxis_fit, *popt), label=r\"$T_2^*$ = %.2f ms | f = %.1f Hz\"%(popt[0], 1e3*popt[1]))\n",
    "        ax.set_xlabel('Ramsey inter pulse delay (ms)')\n",
    "        ax.set_ylabel(r'P$_{|\\uparrow\\rangle}$')\n",
    "        ax.legend()\n",
    "        plt.tight_layout()\n",
    "    \n",
    "\n",
    "plt.figure()\n",
    "mean_t2 = np.mean(t2_list)\n",
    "plt.plot([0,1.2*max(real_measurement_time)],[mean_t2,mean_t2],\"--\", label = \"mean $T_2^* = %i $ ms\"%mean_t2)\n",
    "plt.plot(real_measurement_time,t2_list, \"o\")\n",
    "plt.ylabel(r\"$T_2^*$ (ms)\")\n",
    "plt.xlabel(\"Experiment run time (hours)\")\n",
    "plt.xlim(0,1.05*max(real_measurement_time))\n",
    "plt.legend()\n",
    "           \n",
    "plt.figure()\n",
    "plt.plot(real_measurement_time,frequency_list)\n",
    "plt.ylabel(\"f (kHz)\")\n",
    "plt.xlabel(\"Experiment run time (hours)\")\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(real_measurement_time,np.abs(contrast_list))\n",
    "plt.ylabel(\"Contrast\")\n",
    "plt.xlabel(\"Experiment run time (hours)\")\n",
    "\n",
    "plt.figure()\n",
    "hist = plt.hist(np.concatenate(data_sliced.sum(2)[:,:,0]-data_sliced.sum(2)[:,:,1]), bins=np.arange(-80,80,2),alpha=0.5, label = r\"$|\\uparrow\\rangle$ readout\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.xlabel(\"Differential photon counts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a51d291e-b051-48fe-94c7-ea9505acd648",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc7a9af3-45df-46ee-843b-ee08a66272c4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83706e96-03f8-47eb-a550-e1205ac6d6f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "lowpass = butter(1, 0.2, 'lp', fs=0.5, output='sos')\n",
    "\n",
    "#lowpass = butter(1, (0.001,0.15), 'bp', fs=0.5, output='sos')\n",
    "\n",
    "data_filtered =  sosfilt(lowpass,  data.mean(2).mean(0)[:,0])\n",
    "\n",
    "plt.figure()\n",
    "axe_FFT = plt.gca()\n",
    "tau = times_ramsey*1e-3\n",
    "freq=np.fft.fftfreq(len(tau),d=(tau[1]-tau[0]))\n",
    "y1 = np.fft.fft(data_filtered)\n",
    "y2 = np.fft.fft(data.mean(2).mean(0)[:,0])\n",
    "axe_FFT.plot(freq[1:len(tau)//2],np.abs(y1[1:len(tau)//2]),'r-o')\n",
    "axe_FFT.plot(freq[1:len(tau)//2],np.abs(y2[1:len(tau)//2]),'k-o')\n",
    "axe_FFT.set(xlabel = 'freq (MHz)',ylabel = 'FFT')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b866498-2612-4de0-af45-ec02aebfa2b9",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Nuclear echo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "884cad4a-ffa6-4968-9d0b-85e7339fe060",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3\\\\Nuclear_echo_refocusing_tracking\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = ['20231012214335_\\\\']\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    \n",
    "    directory=os.listdir(path+timestamp)[0]\n",
    "    filename=getfiles_hdf5(path+timestamp+directory)\n",
    "    data=load_h5_to_dic(path+timestamp+directory+r'\\\\'+filename[0])\n",
    "    data_list.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d58bb9f-628d-4a7f-9321-b15172b95f5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "path+timestamp+directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c62109d8-01ae-49e3-9d3f-b6aa730ea782",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename=getfiles_hdf5('Z:\\\\SMPD3-8\\\\SpinRun3\\\\Nuclear_echo_refocusing_tracking\\\\20231012214335_\\\\20231012214335_Nuclear_echo_refocusing_tracking')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cae1ba29-8861-49fe-91f8-ac128b490b64",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "Key_field = '0'\n",
    "times_ramsey=data_list[0][0][Key_field]['times_ramsey']*2\n",
    "data=data_list[0][0][Key_field]['click_array']\n",
    "ramsey_detuning=data_list[0][0][Key_field]['ramsey_detuning']\n",
    "excess = data.mean(2).mean(0)\n",
    "average_number = data.shape[0]\n",
    "\n",
    "def ramsey_osc(t,T,f,alpha,beta,phi,a):\n",
    "    return alpha/2*np.exp(-(t/T))*np.cos(2*np.pi*f*t+2*np.pi*phi) + beta + a*t\n",
    "\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(3,1, figsize=(10, 10))\n",
    "\n",
    "ax[0].plot(times_ramsey*1e-6, data.mean(2).mean(0)[:,0]     , label = r\"${|\\uparrow\\rangle}$\")\n",
    "ax[0].plot(times_ramsey*1e-6, data.mean(2).mean(0)[:,1]     , label = r\"${|\\downarrow\\rangle}$\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Ramsey inter pulse delay (ms)')\n",
    "ax[0].legend()\n",
    "\n",
    "data_singleshot=(data.sum(2)[:,:,0]-data.sum(2)[:,:,1])>0\n",
    "\n",
    "guess=[1e3, 0.5e-3, 2,  0.2, 0, 0]\n",
    "try:\n",
    "    popt, pcov = sp.curve_fit(ramsey_osc, times_ramsey*1e-6, data_singleshot.mean(0), guess)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "    popt = guess\n",
    "    \n",
    "times_ramsey_fit=np.linspace(times_ramsey[0],times_ramsey[-1],201)\n",
    "linear_background = popt[5] * times_ramsey*1e-3 + popt[3]\n",
    "linear_background_fit = popt[5] * times_ramsey_fit*1e-3 + popt[3]\n",
    "\n",
    "ax[1].plot(times_ramsey*1e-6, data_singleshot.mean(0))\n",
    "ax[1].plot(times_ramsey_fit*1e-6, ramsey_osc(times_ramsey_fit*1e-6, *popt), label=r\"$T_2^*$ = %.2f s | f = %.3f Hz\"%(popt[0]*1e-3, popt[1]*1e3))\n",
    "ax[1].set_xlabel('Ramsey inter pulse delay (ms)')\n",
    "ax[1].set_ylabel(r'P$_{|\\uparrow\\rangle}$')\n",
    "ax[1].legend()\n",
    "\n",
    "############# FFT #############\n",
    "x = times_ramsey*1e-3\n",
    "y = data_singleshot.mean(0)-0.5\n",
    "\n",
    "x_freq=np.fft.fftfreq(len(x),d=(x[1]-x[0]))\n",
    "y_fft = np.abs(np.fft.rfft(y))\n",
    "\n",
    "ax[2].plot(1e3*x_freq[:len(x_freq)//2],y_fft[:len(x_freq)//2])\n",
    "ax[2].set_xlim(0,None)\n",
    "ax[2].set_xlabel(\"frequency (kHz)\")\n",
    "ax[2].set_ylabel(r\"FFT (P$_{|\\uparrow\\rangle}$)\")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a645666c-1ea7-495e-a673-91b3900a108b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_function(guess,func,xdata,ydata,lb = -np.inf,ub = np.inf, extend = False):\n",
    "    \"\"\"Wrapper around curve_fit to conveniently do all the stuff you wish it already did\"\"\"\n",
    "    \n",
    "    est,cov = curve_fit(func, xdata, ydata,p0 = guess, bounds = (lb, ub))\n",
    "    std = np.sqrt(np.diag(cov))\n",
    "    #print (est,std)\n",
    "    #\n",
    "    if not extend:\n",
    "        fine = np.linspace(min(xdata),max(xdata),len(xdata)*10)\n",
    "        data_fit=func(fine,*est)\n",
    "        return (est,std,fine,data_fit)\n",
    "    else:\n",
    "        delta = max(xdata) - min(xdata)\n",
    "        fine = np.linspace(min(xdata) - extend*delta,max(xdata) + extend*delta,len(xdata)*10*(2*extend+1))\n",
    "        data_fit=func(fine,*est)\n",
    "        return (est,std,fine,data_fit)\n",
    "    \n",
    "from scipy.optimize import curve_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04a88918-4d9e-4fd1-8975-bb6700bbfb84",
   "metadata": {},
   "outputs": [],
   "source": [
    "def decaying_sin(x,a,b,c,d,e):\n",
    "    return(a+b*np.sin(c*x+d)*np.exp(-x/e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4ca77a1-ebe1-40d3-871d-baaa6ba1b82a",
   "metadata": {},
   "outputs": [],
   "source": [
    "est,std,fine,data_fit = fit_function([0.4,0.2,2e-3,-np.pi/2,4000],decaying_sin,2*times_ramsey*1e-6,data_singleshot.mean(0))\n",
    "\n",
    "plt.figure(figsize = (12,6))\n",
    "\n",
    "data_singleshot=(data.sum(2)[:,:,0]-data.sum(2)[:,:,1])>0\n",
    "plt.plot(2*times_ramsey*1e-6, data_singleshot.mean(0), \"o\")\n",
    "plt.plot(fine,data_fit, label = r\"$T_{2}^{echo} = %.1f(%i)$ s\"%(1e-3*est[4], 1e-2*std[4]), color='k', alpha=0.5)\n",
    "plt.xlabel(r'2 $\\tau_{echo}$ (ms)')\n",
    "plt.ylabel(r'P$_{|\\uparrow\\rangle}$')\n",
    "plt.ylim([0,1])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "094fcdf2-b339-4e93-9f63-1d856dfa30a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "7500.0e6-1.4e6-0.46e6-1e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62aefaf3-4443-4f20-9b2d-ede8c577185b",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from scipy.signal import savgol_filter\n",
    "Key_field = '0'\n",
    "times_ramsey=data_list[0][0][Key_field]['times_ramsey']\n",
    "data        =data_list[0][0][Key_field]['click_array']\n",
    "\n",
    "window = 5\n",
    "order = 2\n",
    "\n",
    "ramsey_detuning=data_list[0][0][Key_field]['ramsey_detuning']\n",
    "excess = data.mean(2).mean(0)\n",
    "\n",
    "average_number = data.shape[0]\n",
    "\n",
    "def ramsey_osc(t,T,f,alpha,beta,phi,a):\n",
    "    return alpha/2*np.exp(-(t/T))*np.cos(2*np.pi*f*t+2*np.pi*phi) + beta + a*t\n",
    "\n",
    "\n",
    "slices = 5\n",
    "slice_size = data.shape[0]//slices\n",
    "real_measurement_time = np.linspace(0,13.16,slices)+56/60/2\n",
    "\n",
    "frequency_list = []\n",
    "t2_list = []\n",
    "contrast_list = []\n",
    "for i in range(slices):\n",
    "    \n",
    "    data_sliced = data[(i*slice_size):(((i+1)*slice_size)%data.shape[0])]\n",
    "    ############################### Plotting ###############################\n",
    "\n",
    "    data_singleshot=(data_sliced.sum(2)[:,:,0]-data_sliced.sum(2)[:,:,1])>0\n",
    "    \n",
    "    up_state_prob = data_singleshot.mean(0)\n",
    "    time_xaxis    = 2*times_ramsey*1e-6      \n",
    "    \n",
    "    guess=[4e3, 0.25e-3, 0.5,  0.2, -0.25, 0]\n",
    "    try:\n",
    "        popt, pcov = sp.curve_fit(ramsey_osc, time_xaxis, up_state_prob, guess)\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        popt = guess\n",
    "\n",
    "    times_xaxis_fit=np.linspace(time_xaxis[0],time_xaxis[-1],201)\n",
    "    linear_background = popt[5] * time_xaxis + popt[3]\n",
    "    linear_background_fit = popt[5] * time_xaxis + popt[3]\n",
    "    \n",
    "    contrast_list.append(popt[2])\n",
    "    t2_list.append(popt[0])\n",
    "    frequency_list.append(popt[1])\n",
    "    \n",
    "    if True and i%1== 0:\n",
    "        fig,ax=plt.subplots(1,1, figsize=(10, 3))\n",
    "        ax.set_title('Slice number: %d'%i)\n",
    "        ax.plot(time_xaxis, up_state_prob)\n",
    "        ax.plot(times_xaxis_fit, ramsey_osc(times_xaxis_fit, *popt), label=r\"$T_2^*$ = %.2f ms | f = %.1f Hz\"%(popt[0], 1e3*popt[1]))\n",
    "        ax.set_xlabel('Ramsey inter pulse delay (ms)')\n",
    "        ax.set_ylabel(r'P$_{|\\uparrow\\rangle}$')\n",
    "        ax.legend()\n",
    "        plt.tight_layout()\n",
    "    \n",
    "\n",
    "plt.figure()\n",
    "mean_t2 = np.mean(t2_list)\n",
    "plt.plot([0,1.2*max(real_measurement_time)],[mean_t2,mean_t2],\"--\", label = \"mean $T_2^* = %i $ ms\"%mean_t2)\n",
    "plt.plot(real_measurement_time,t2_list, \"o\")\n",
    "plt.ylabel(r\"$T_2^*$ (ms)\")\n",
    "plt.xlabel(\"Experiment run time (hours)\")\n",
    "plt.xlim(0,1.05*max(real_measurement_time))\n",
    "plt.legend()\n",
    "           \n",
    "plt.figure()\n",
    "plt.plot(real_measurement_time,frequency_list)\n",
    "plt.ylabel(\"f (kHz)\")\n",
    "plt.xlabel(\"Experiment run time (hours)\")\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(real_measurement_time,np.abs(contrast_list))\n",
    "plt.ylabel(\"Contrast\")\n",
    "plt.xlabel(\"Experiment run time (hours)\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caeb5da8-fdbc-4c95-ad4a-255b5d834ab4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-06T08:26:45.265349Z",
     "iopub.status.busy": "2023-10-06T08:26:45.265349Z",
     "iopub.status.idle": "2023-10-06T08:26:45.305358Z",
     "shell.execute_reply": "2023-10-06T08:26:45.305358Z",
     "shell.execute_reply.started": "2023-10-06T08:26:45.265349Z"
    }
   },
   "source": [
    "# Nuclear T1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f550e9ca-43d7-45e7-9851-e4e0436959be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5233eb9-cb71-4f6d-bd06-5239b9a030dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3\\\\Nuclear_T1\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = ['20231005235944_\\\\']\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    directory=os.listdir(path+timestamp)[0]\n",
    "    filename=getfiles_hdf5(path+timestamp+directory)\n",
    "    data=load_h5_to_dic(path+timestamp+directory+r'\\\\'+filename[0])\n",
    "    data_list.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "414137c1-482d-44ac-9663-52aa8f78af0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "Key_field = '0'\n",
    "data=data_list[0][0][Key_field]['click_array']\n",
    "times_t1=data_list[0][0][Key_field]['times_ramsey']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23754e65-7687-446c-8089-ccc31ecf5cb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_singleshot=(data.sum(2)[:,:,0]-data.sum(2)[:,:,1])>0\n",
    "plt.plot(times_t1*1e-9, data_singleshot.mean(0), \"o\")\n",
    "plt.xlabel('Readout delay (s)')\n",
    "plt.ylabel(r'P$_{|\\uparrow\\rangle}$')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c382763-9daf-4f1b-8035-69604e769c4e",
   "metadata": {},
   "source": [
    "# Raman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "902c112c-b5c5-462f-b991-88d8362f53ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "path='Z:\\\\SMPD3-8\\\\SpinRun3\\\\sideband_ramsey\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = ['20231017234039_\\\\']\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    directory=os.listdir(path+timestamp)[0]\n",
    "    filename=getfiles_hdf5(path+timestamp+directory)\n",
    "    data=load_h5_to_dic(path+timestamp+directory+r'\\\\'+filename[0])\n",
    "    data_list.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39c71602-d435-4751-9cb8-c3252407bb7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "Key_field = '0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a86b6544-8108-45ea-ba6f-f470c4e634da",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data_list[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5e3806d-ce1c-49ac-9aa0-707389f5876e",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_guess = 0\n",
    "\n",
    "lorentz_func = lambda x,*y: y[0]/((x-y[2])**2+y[1]**2)+y[3]\n",
    "data=data_list[0][0][Key_field]['click_array']\n",
    "freqs=data_list[0][0][Key_field]['freqs']\n",
    "freq_sideband=data_list[0][0][Key_field]['freq_sideband']\n",
    "\n",
    "x = (freqs-freq_sideband)*1e-3\n",
    "data_singleshot=(data.sum(2)[:,:,0]-data.sum(2)[:,:,1])>0\n",
    "y = data_singleshot.mean(0)\n",
    "guess=[max(y)*0.0001, 0.01, x[np.argmax(y)], 0]\n",
    "est,std,fine,data_fit = fit_function(guess,lorentz_func,x,y)\n",
    "\n",
    "fig,ax=plt.subplots(2,1,tight_layout=True)\n",
    "\n",
    "ax[0].plot(x, data.mean(2).mean(0)[:,0], label = r\"${|\\uparrow\\rangle}$\")\n",
    "ax[0].plot(x, data.mean(2).mean(0)[:,1], label = r\"${|\\downarrow\\rangle}$\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Detuning (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].plot(x, y, '.', color='indianred')\n",
    "ax[1].set_xlabel('Detuning (kHz)')\n",
    "ax[1].set_ylabel(r'P$_{|\\uparrow\\rangle}$')\n",
    "kappa = abs(2*est[1])\n",
    "ax[1].plot(fine,data_fit, \"k\",alpha = 0.5, label = r\"$\\kappa = %.3f(%i)$ kHz\"%(kappa, 1000*std[1]))\n",
    "if plot_guess: ax[1].plot(fine,lorentz_func(fine, *guess), \"k\",alpha = 0.5, label = r\"guess\")\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(path+\"20231017234039_Raman_spectroscopy_nuclear_spin.pdf\")\n",
    "np.savetxt(path+\"20231017234039_Raman_spectroscopy_nuclear_spin.txt\", np.transpose([x,y]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "007f833d-1b8e-4f7e-a431-7b947a8ac5cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "path+\"20231017234039_Raman_spectroscopy_nuclear_spin.pdf\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0fd4820-8b66-4ebc-ac46-9f91421bde65",
   "metadata": {},
   "outputs": [],
   "source": [
    "max(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b8e0835-c979-46b0-9e79-c020cbd8f7ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(-min(x)+est[2]+0.7)\n",
    "print(max(x)-est[2]-0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36de256e-bff9-4155-ac8a-61f2f16420a6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7ac04a0d-4136-4d34-9a24-b0f09faf4b65",
   "metadata": {},
   "source": [
    "# Raman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c676d1f-620c-481e-bb72-b7e45cecbcb9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b3218be-bf9f-4cfb-b95f-000ae1c0dc17",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3\\\\raman_rabi\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = ['20231121201719_\\\\']\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    directory=os.listdir(path+timestamp)[0]\n",
    "    filename=getfiles_hdf5(path+timestamp+directory)\n",
    "    data=load_h5_to_dic(path+timestamp+directory+r'\\\\'+filename[0])\n",
    "    data_list.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1543e83-972d-43aa-8977-7cc58d0bcd59",
   "metadata": {},
   "outputs": [],
   "source": [
    "lorentz_func = lambda x,*y: y[0]/((x-y[2])**2+y[1]**2)+y[3]\n",
    "Key_field='0'\n",
    "data=data_list[0][0][Key_field]['click_array']\n",
    "freqs=data_list[0][0][Key_field]['freq_range']\n",
    "freq_sideband=data_list[0][0][Key_field]['freq_sideband']\n",
    "\n",
    "x = (freqs-freq_sideband)*1e-3\n",
    "data_singleshot=(data.sum(2)[:,:,0]-data.sum(2)[:,:,1])>0\n",
    "y = data_singleshot.mean(0)\n",
    "guess=[max(y)*0.0001, 0.01, x[np.argmax(y)], 0]\n",
    "est,std,fine,data_fit = fit_function(guess,lorentz_func,x,y)\n",
    "\n",
    "fig,ax=plt.subplots(2,1,tight_layout=True)\n",
    "\n",
    "ax[0].plot(x, data.mean(2).mean(0)[:,0], label = r\"${|\\uparrow\\rangle}$\")\n",
    "ax[0].plot(x, data.mean(2).mean(0)[:,1], label = r\"${|\\downarrow\\rangle}$\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Detuning (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].plot(x, y, '.', color='indianred')\n",
    "ax[1].set_xlabel('Detuning (kHz)')\n",
    "ax[1].set_ylabel(r'P$_{|\\uparrow\\rangle}$')\n",
    "kappa = abs(2*est[1])\n",
    "ax[1].plot(fine,lorentz_func(fine, *guess), \"k\",alpha = 0.5, label = r\"$\\kappa = %.3f(%i)$ kHz\"%(kappa, 1000*std[1]))\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6862f5b-9f19-4564-9747-3aaa10f4ddb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "###########################################################\n",
    "%matplotlib widget\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(3,1, figsize=(6,6))\n",
    "x = (np.array(freqs)-freq_sideband)*1e-3\n",
    "\n",
    "ax[0].plot(x, (data.sum(2)>105).mean(0)     , label = r\"${|\\downarrow\\downarrow\\rangle}$\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c09fea3-d513-4405-9c82-d9cc3c1af578",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc465bd7-9f53-467a-84eb-bb8d63d866cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0ab3e60-989c-450c-a659-bc1e06de6f84",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(x[:100], ((data[:,:100]).sum(2)>105).mean(0)     , label = r\"${|\\downarrow\\downarrow\\rangle}$\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da21ed38-d4aa-45e9-b59d-df47796393b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3\\\\sideband_ramsey\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = ['20231018163511_\\\\']\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    directory=os.listdir(path+timestamp)[0]\n",
    "    filename=getfiles_hdf5(path+timestamp+directory)\n",
    "    data=load_h5_to_dic(path+timestamp+directory+r'\\\\'+filename[0])\n",
    "    data_list.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b794643-d30c-4e01-8df5-6dc29fb6ae9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_list[0][0][Key_field]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73d0ffd5-28da-41f5-b854-0b8fab5c2467",
   "metadata": {},
   "source": [
    "# Sideband spectroscopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80e6b3f3-9ca0-4531-9246-1828ceabdab5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1400 kHz high power, large range, b flipped\n",
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\sideband_spectro\\\\'\n",
    "timestamp_list = grab_timestamps_hdf(path, \"20240124212528\", \"20240125013000\") # Red sideband\n",
    "#timestamp_list = grab_timestamps_hdf(path, \"20240125232314\", \"20240126022428\") # Blue sideband\n",
    "\n",
    "data_list = load_from_directory(path, timestamp_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "736bb700-9721-4b1a-b42d-288db92497a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "fits = []\n",
    "plot_guess = 0\n",
    "fig0,ax0=plt.subplots(1,1,figsize=(10,5),tight_layout=True)\n",
    "amps = np.linspace(0.1,0.4,4)\n",
    "for i in range(len(data_list)):\n",
    "    Key_field = list(data_list[i][0].keys())[0]\n",
    "    data           = data_list[i][0][Key_field]['click_array']\n",
    "    sideband_freqs = data_list[i][0][Key_field]['sideband_freqs']\n",
    "    centre_freq = 0\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "    sideband_freqs = np.array(sideband_freqs)\n",
    "    x = (sideband_freqs - Photon_IF)*1e-3-centre_freq\n",
    "    xlabel = \"Frequency [kHz]\"\n",
    "    \n",
    "    fig,ax=plt.subplots(1,1,figsize=(10,3),tight_layout=True)\n",
    "\n",
    "    p_data = (data[:,:,-1]>100).mean(0)\n",
    "    if len(readout_freqs)==4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        pops = [p_data]\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    ### Plot 2 - Probability of the state ###\n",
    "    for l, pop in zip(labels, pops): \n",
    "        ax.plot(x, pop, \"o-\", label = l)\n",
    "\n",
    "    ax.set_xlabel(xlabel)\n",
    "    ax.set_ylabel(\"Population\")\n",
    "    ax.set_ylim(0,1)\n",
    "    ax.legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "    p_data = pops[1]\n",
    "    guess = [x[np.argmax(p_data)],(max(x)-min(x))/8, -min(p_data)+max(p_data), min(p_data)]\n",
    "    try: \n",
    "        est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "        fit_success = 1\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        fit_success = 0\n",
    "        fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "    if fit_success: \n",
    "        ax.plot(fine,data_fit, 'k', label = \"centre freq = %.3f kHz\"%est[0])\n",
    "        sideband_frequency_meas = est[0]*1e3\n",
    "    if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "    ax.legend()\n",
    "    \n",
    "    fits.append(est)\n",
    "    \n",
    "    ax0.plot(x, p_data,'o-')\n",
    "    ax0.plot(fine,data_fit, 'k', label = \"amp = %.1f | centre freq = %.3f kHz\"%(amps[i], est[0]))\n",
    "    ax0.set_ylabel(\"Population\")\n",
    "    ax0.set_ylim(0,1)\n",
    "    \n",
    "ax0.legend() \n",
    "fits = np.array(fits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3952c202-aca2-404b-b9d4-2310fe166b76",
   "metadata": {},
   "outputs": [],
   "source": [
    "sb_freq_red = freqs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96e2d6f3-29b0-4271-8822-305ddce24a1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lorentz_filter(delta, kappa):\n",
    "    return 1 / np.sqrt(1+4*delta**2/kappa**2)\n",
    "\n",
    "def rabi_freq_from_amp(amp, freq):\n",
    "    return 106.8 * amp/0.05 * lorentz_filter(freq, 700)\n",
    "\n",
    "amps = np.linspace(0.1, 0.4, 4)\n",
    "freqs = fits[:, 0]\n",
    "\n",
    "rabi_freqs = rabi_freq_from_amp(amps, freqs)\n",
    "\n",
    "vals = np.polyfit(rabi_freqs**2, freqs, 1)\n",
    "freq0 = np.polyval(vals, 0)\n",
    "delta_ac = freqs-freq0\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "plt.xlabel(r'$(\\Omega_R / 2\\pi)^2$ [kHz$^2$]')\n",
    "plt.ylabel('$\\Delta_{AC}$ (kHz)')\n",
    "plt.plot(rabi_freqs**2, delta_ac, 'ro', label='Red sideband')\n",
    "plt.plot(rabi_freqs**2, np.polyval(vals, rabi_freqs**2)-freq0, 'k--', alpha=0.2)\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "plt.xlabel(r'$\\Omega_R / 2\\pi$ (kHz)')\n",
    "plt.ylabel('$\\delta$ (kHz)')\n",
    "plt.plot(rabi_freqs, fits[:, 1], 'o-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efc818e5-35aa-4efc-9263-c56f043c0161",
   "metadata": {},
   "outputs": [],
   "source": [
    "rabi_freqs0 = rabi_freqs\n",
    "delta_ac0 = delta_ac\n",
    "vals0 = vals\n",
    "freq00 = freq0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b06d7c01-09eb-40cb-b1ce-53718050d1dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.xlabel(r'$(\\Omega_R / 2\\pi)^2$ [kHz$^2$]')\n",
    "plt.ylabel('$|\\Delta_{AC}|$ (kHz)')\n",
    "\n",
    "plt.plot(rabi_freqs0**2, -delta_ac0, 'ro', label='Red sideband')\n",
    "plt.plot(rabi_freqs0**2, -np.polyval(vals0, rabi_freqs0**2)+freq00, 'k--', alpha=0.2)\n",
    "plt.legend()\n",
    "\n",
    "plt.plot(rabi_freqs**2, delta_ac, 'bo', label='Blue sideband')\n",
    "plt.plot(rabi_freqs**2, np.polyval(vals, rabi_freqs**2)-freq0, 'k--', alpha=0.2)\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f26430ef-d318-49ab-9d3c-939a64a7046a",
   "metadata": {},
   "source": [
    "# Sideband rabi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "639357c0-4856-44d8-9ac8-023add1d8428",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\sideband_rabi\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = [\n",
    "    '20231214135531',\n",
    "]\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    filenames=getfiles_hdf5(path)\n",
    "    for filename in filenames:\n",
    "        if timestamp in filename: \n",
    "            print('Loading '+path+filename)\n",
    "            data=load_h5_to_dic(path+filename)\n",
    "            data_list.append(data)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1077d5a-b30f-4d2b-a287-4638ed4d0ed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1400 kHz high power, large range, b flipped\n",
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\sideband_rabi\\\\'\n",
    "timestamp_list = grab_timestamps_hdf(path, \"20240127122859\", \"20240127142948\") # Red sideband\n",
    "#timestamp_list = grab_timestamps_hdf(path, \"20240128174952\", \"20240128205101\") # Blue sideband\n",
    "\n",
    "data_list = load_from_directory(path, timestamp_list)\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95fcfea6-0015-41f0-afb7-d2cf753eb77d",
   "metadata": {},
   "outputs": [],
   "source": [
    "fits = []\n",
    "plot_guess = 0\n",
    "fig0,ax0=plt.subplots(1,1,figsize=(10,5),tight_layout=True)\n",
    "amps = np.linspace(0.1,0.4,4)\n",
    "\n",
    "guess = [0.01,100,0.5,1,1*np.pi]\n",
    "for i in range(len(data_list)):\n",
    "    Key_field = list(data_list[i][0].keys())[0]\n",
    "    data = data_list[i][0][Key_field]['click_array']\n",
    "    readout_freqs=data_list[i][0][Key_field]['readout_freqs']\n",
    "    rabi_durations=data_list[i][0][Key_field]['rabi_durations']\n",
    "    \n",
    "    corrected_rabi_durations = adiabatic_spin_sigma*(adiabatic_spin_Nsigma)+rabi_durations # Effective pulse duration including sigmoids\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "    x = 1e-3*np.array(corrected_rabi_durations)\n",
    "    xlabel = \"Rabi durations [us]\"\n",
    "    \n",
    "    fig,ax=plt.subplots(1,1,figsize=(10,3),tight_layout=True)\n",
    "\n",
    "    p_data = (data[:,:,-1]>100).mean(0)\n",
    "    if len(readout_freqs)==4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        pops = [p_data]\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    ### Plot 2 - Probability of the state ###\n",
    "    for l, pop in zip(labels, pops): \n",
    "        ax.plot(x, pop, \"o-\", label = l)\n",
    "\n",
    "    ax.set_xlabel(xlabel)\n",
    "    ax.set_ylabel(\"Population\")\n",
    "    ax.set_ylim(0,1)\n",
    "    ax.legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "    \n",
    "    p_data = pops[-1]\n",
    "    try: \n",
    "        est,std,fine,data_fit = fit_function(guess, rabi_fit, x, p_data)\n",
    "        fit_success = 1\n",
    "        guess = est*1.2\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        fit_success = 0\n",
    "        fine = np.linspace(x[0],x[-1],len(x))\n",
    "\n",
    "    if fit_success: \n",
    "        ax.plot(fine,data_fit, 'k', label = \"centre freq = %.3f kHz\"%est[0])\n",
    "        sideband_frequency_meas = est[0]*1e3\n",
    "        \n",
    "    if plot_guess: ax.plot(fine,rabi_fit(fine,*guess))\n",
    "    ax.legend()\n",
    "    \n",
    "    fits.append(est)\n",
    "    \n",
    "    ax0.plot(x, p_data,'o-')\n",
    "    #ax0.plot(fine,data_fit, 'k', label = \"amp = %.1f | centre freq = %.3f kHz\"%(amps[i], est[0]))\n",
    "    ax0.set_ylabel(\"Population\")\n",
    "    ax0.set_ylim(0,1)\n",
    "    \n",
    "ax0.legend() \n",
    "fits = np.array(fits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6b5b4fb-0f82-4070-85be-473b53c25454",
   "metadata": {},
   "outputs": [],
   "source": [
    "rabi_freq_from_amp(amps, sb_freq_blue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca4d3e1d-a655-4999-a8b5-363a76703239",
   "metadata": {},
   "outputs": [],
   "source": [
    "lorentz_filter(sb_freq_blue, 700)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4c029fc-c6b6-4ee0-85f0-0ef408a0ade7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lorentz_filter(delta, kappa):\n",
    "    return 1 / np.sqrt(1+4*delta**2/kappa**2)\n",
    "\n",
    "def rabi_freq_from_amp(amp, freq):\n",
    "    return 106.8 * amp/0.05 * lorentz_filter(freq, 700)\n",
    "\n",
    "amps = np.linspace(0.1, 0.3, 3)\n",
    "freqs = fits[:, 0]*1e3\n",
    "\n",
    "rabi_freqs = rabi_freq_from_amp(amps, sb_freq_red[:3])\n",
    "\n",
    "plt.figure()\n",
    "plt.xlabel(r'$\\Omega_R / 2\\pi$ [kHz]')\n",
    "plt.ylabel('$\\Omega_{SB}/ 2\\pi$ (kHz)')\n",
    "plt.plot(rabi_freqs**1, freqs, 'o-')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b66290fe-f4d6-45a3-ada7-2b36548db5b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "rabi_freqs0 = rabi_freqs\n",
    "freqs0 = freqs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65476628-505e-456f-98c2-649cd00a38c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.xlabel(r'$\\Omega_R / 2\\pi$ [kHz]')\n",
    "plt.ylabel('$\\Omega_{SB}/ 2\\pi$ (kHz)')\n",
    "\n",
    "\n",
    "plt.plot(rabi_freqs0, freqs0, 'bo-', label='Blue sideband')\n",
    "plt.plot(rabi_freqs, freqs, 'ro-', label='Red sideband')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "983f3769-98c6-47a6-9290-69299edb8fb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.xlabel(r'$|\\Delta_{AC}|$ (kHz)')\n",
    "plt.ylabel('$(\\Omega_{SB}/ 2\\pi)^2 $ (kHz^2)')\n",
    "\n",
    "\n",
    "plt.plot(np.abs(delta_ac), freqs0**2, 'bo', label='Blue sideband')\n",
    "plt.plot(np.abs(delta_ac0), freqs**2, 'ro', label='Red sideband')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "935d4624-e125-4936-8020-d0311e2c1f34",
   "metadata": {},
   "outputs": [],
   "source": [
    "freqs0.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2e7b1ac-6472-4369-bc0b-f8fcf76a2aa4",
   "metadata": {},
   "source": [
    "# Frequency tracking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b735e00-b143-4f6b-bb19-57c8784bd6ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\Spectroscopy_frequency_sweep_fixed_field\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = [\n",
    "    '20240203223035'\n",
    "]\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    filenames=getfiles_hdf5(path)\n",
    "    for filename in filenames:\n",
    "        if timestamp in filename: \n",
    "            print('Loading '+path+filename)\n",
    "            data=load_h5_to_dic(path+filename)\n",
    "            data_list.append(data)\n",
    "    \n",
    "def lorentz(delta,delta0,kappa,a,b):\n",
    "    return a/(1+(delta-delta0)**2/(kappa/2)**2)+b\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5721b2b-452b-4172-bddf-82ee7952fe8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "mins = 1346-1051"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64905bf9-a717-4504-be40-e08a84ae1a73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial plot and extraction of data. This plots the data exactly as is from \n",
    "# SingleSpin notebook and serves as a reminder of what are we working with\n",
    "\n",
    "Key_field = list(data_list[0][0].keys())[0]\n",
    "time_axis=data_list[0][0][Key_field]['time_axis']\n",
    "click_array=data_list[0][0][Key_field]['click_array']\n",
    "freq_range=data_list[0][0][Key_field]['freq_range']\n",
    "amplitude_pulse=data_list[0][0][Key_field]['amplitude_pulse']\n",
    "\n",
    "N = 30\n",
    "chunk = 200\n",
    "\n",
    "average_number = click_array.shape[0]\n",
    "cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "\n",
    "%matplotlib inline\n",
    "integration_index=5\n",
    "integration_index_bg=5\n",
    "excess=(click_array.mean(0)[:,:integration_index].sum(-1)-0*click_array.mean(0)[:,-integration_index_bg:].mean(-1)*integration_index)*(time_hist[1]-time_hist[0])\n",
    "background = click_array.mean(0)[:,-integration_index_bg:].mean(-1)*(time_hist[1]-time_hist[0])\n",
    "#print((freq_range-Photon_IF)/1e3)\n",
    "plt.plot((freq_range-Photon_IF)/1e3,excess,'-o')\n",
    "plt.title('average number %s \\n cycle time %.1f us \\n Spin_const_amp =  %.3f \\n amplitude_pulse = %.3f'%(average_number, cycle_time*1e3, Spin_Gauss_amplitude, amplitude_pulse))\n",
    "plt.xlabel('Frequency offset (kHz)')\n",
    "plt.ylabel('integrated excess counts')\n",
    "#plt.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "############### Plot Background ###############\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.plot((freq_range-Photon_IF)/1e3,background,'-o')\n",
    "plt.title('average number %s \\n cycle time %.1f us '%(average_number, cycle_time*1e3))\n",
    "plt.xlabel('Frequency offset (kHz)')\n",
    "plt.ylabel('background counts')\n",
    "#plt.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "############### Plot decay ###############\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.plot(time_hist,click_array.mean(1).mean(0),'-o')\n",
    "plt.xlabel('time (ms)')\n",
    "plt.ylabel('count rate (/ms)')\n",
    "plt.tight_layout()\n",
    "\n",
    "def exp_decay(t,T,alpha,beta):\n",
    "    return alpha*np.exp(-t/T)+beta\n",
    "\n",
    "try:\n",
    "    popt, pcov = sp.curve_fit(exp_decay, time_hist, click_array.mean(1).mean(0),[1,0.1,5])\n",
    "    label=\"$T_1$ = %.2f ms\"%(popt[0])\n",
    "    plt.plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "    plt.legend()\n",
    "\n",
    "except:\n",
    "    print('fit failed')\n",
    "\n",
    "npts = np.round(click_array.shape[0],-3)\n",
    "offset = 0\n",
    "integration_index=10\n",
    "integration_index_bg=1\n",
    "plt.figure()\n",
    "excess_array = []\n",
    "t_step = time_hist[1]-time_hist[0]\n",
    "for start in offset+np.linspace(0,npts-chunk,npts//chunk,dtype = int):\n",
    "    PrintStatic(str(start))\n",
    "    stop = start+chunk\n",
    "\n",
    "    excess=(click_array[start:stop].mean(0)[:,:integration_index].sum(-1))*t_step\n",
    "\n",
    "    excess_array.append(excess)\n",
    "    # background = click_array.mean(0)[:,-integration_index_bg:].mean(-1)*(time_hist[1]-time_hist[0])\n",
    "\n",
    "    # plt.figure()\n",
    "    if start%10000==0: \n",
    "\n",
    "        plt.plot((freq_range-Photon_IF)/1e3,excess,'-o',label = \"%.1f\"%((freq_range[np.argmax(excess)]-Photon_IF)/1e3))# label = \"%i-%i\"%(start,stop))\n",
    "PrintStatic(\"\")\n",
    "\n",
    "plt.title('average number %s \\n cycle time %.1f us \\n Spin_const_amp =  %.3f \\n amplitude_pulse = %.3f'%(average_number, cycle_time*1e3, Spin_Gauss_amplitude, amplitude_pulse))\n",
    "plt.xlabel('Frequency offset (kHz)')\n",
    "plt.ylabel('integrated excess counts')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "excess_array = np.array(excess_array)\n",
    "\n",
    "excess_array.shape\n",
    "\n",
    "################### 2D Plot ###################\n",
    "total_time_freqsweep = mins*60\n",
    "use_time = True\n",
    "chunk_index = int(chunk/2)+np.linspace(0,npts-chunk,npts//chunk,dtype = int) # Use this for y axis if you want to plot chunk centre index\n",
    "time_elapsed = np.linspace(0, total_time_freqsweep/60, len(chunk_index)).round(1)\n",
    "plt.figure(figsize = (7,5.5))\n",
    "if use_time:\n",
    "    yaxis = time_elapsed\n",
    "    ylabel = \"Time (minutes)\"\n",
    "else:\n",
    "    yaxis = chunk_index\n",
    "    ylabel = 'Slice centre index'\n",
    "\n",
    "plot_2d_sweep(excess_array,x=[int(f) for f in (freq_range-Photon_IF)/1e3],y=yaxis,\n",
    "              xlabel = 'Frequency offset (kHz)',ylabel = ylabel,clabel = 'integrated excess counts',xtick = 6,ytick = 82,\n",
    "            cmap = \"Blues\",horizontal_ticks = True, fontsize = None)\n",
    "plt.tight_layout()\n",
    "plt.savefig(path+\"frequency_sweep_time_resolved.png\")\n",
    "\n",
    "np.savetxt(path+\"2dplot_data_xaxis.txt\",np.round((freq_range-Photon_IF)/1e3,0))\n",
    "np.savetxt(path+\"2dplot_data_yaxis.txt\",yaxis)\n",
    "np.savetxt(path+\"2dplot_data_zaxis.txt\",excess_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "818b80b0-83a1-4063-b897-ce66e5f83646",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial plot and extraction of data. This plots the data exactly as is from \n",
    "# SingleSpin notebook and serves as a reminder of what are we working with\n",
    "\n",
    "Key_field = list(data_list[0][0].keys())[0]\n",
    "time_axis=data_list[0][0][Key_field]['time_axis']\n",
    "click_array=data_list[0][0][Key_field]['click_array']\n",
    "freq_range=data_list[0][0][Key_field]['freq_range']\n",
    "amplitude_pulse=data_list[0][0][Key_field]['amplitude_pulse']\n",
    "\n",
    "N = 30\n",
    "chunk = 200\n",
    "\n",
    "average_number = click_array.shape[0]\n",
    "cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "\n",
    "%matplotlib inline\n",
    "integration_index=5\n",
    "integration_index_bg=5\n",
    "excess=(click_array.mean(0)[:,:integration_index].sum(-1)-0*click_array.mean(0)[:,-integration_index_bg:].mean(-1)*integration_index)*(time_hist[1]-time_hist[0])\n",
    "background = click_array.mean(0)[:,-integration_index_bg:].mean(-1)*(time_hist[1]-time_hist[0])\n",
    "#print((freq_range-Photon_IF)/1e3)\n",
    "plt.plot((freq_range-Photon_IF)/1e3,excess,'-o')\n",
    "plt.title('average number %s \\n cycle time %.1f us \\n Spin_const_amp =  %.3f \\n amplitude_pulse = %.3f'%(average_number, cycle_time*1e3, Spin_Gauss_amplitude, amplitude_pulse))\n",
    "plt.xlabel('Frequency offset (kHz)')\n",
    "plt.ylabel('integrated excess counts')\n",
    "#plt.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "############### Plot Background ###############\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.plot((freq_range-Photon_IF)/1e3,background,'-o')\n",
    "plt.title('average number %s \\n cycle time %.1f us '%(average_number, cycle_time*1e3))\n",
    "plt.xlabel('Frequency offset (kHz)')\n",
    "plt.ylabel('background counts')\n",
    "#plt.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "############### Plot decay ###############\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.plot(time_hist,click_array.mean(1).mean(0),'-o')\n",
    "plt.xlabel('time (ms)')\n",
    "plt.ylabel('count rate (/ms)')\n",
    "plt.tight_layout()\n",
    "\n",
    "def exp_decay(t,T,alpha,beta):\n",
    "    return alpha*np.exp(-t/T)+beta\n",
    "\n",
    "try:\n",
    "    popt, pcov = sp.curve_fit(exp_decay, time_hist, click_array.mean(1).mean(0),[1,0.1,5])\n",
    "    label=\"$T_1$ = %.2f ms\"%(popt[0])\n",
    "    plt.plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "    plt.legend()\n",
    "\n",
    "except:\n",
    "    print('fit failed')\n",
    "\n",
    "npts = np.round(click_array.shape[0],-3)\n",
    "offset = 0\n",
    "integration_index=10\n",
    "integration_index_bg=1\n",
    "plt.figure()\n",
    "excess_array = []\n",
    "t_step = time_hist[1]-time_hist[0]\n",
    "for start in offset+np.linspace(0,npts-chunk,npts//chunk,dtype = int):\n",
    "    PrintStatic(str(start))\n",
    "    stop = start+chunk\n",
    "\n",
    "    excess=(click_array[start:stop].mean(0)[:,:integration_index].sum(-1))*t_step\n",
    "\n",
    "    excess_array.append(excess)\n",
    "    # background = click_array.mean(0)[:,-integration_index_bg:].mean(-1)*(time_hist[1]-time_hist[0])\n",
    "\n",
    "    # plt.figure()\n",
    "    if start%10000==0: \n",
    "\n",
    "        plt.plot((freq_range-Photon_IF)/1e3,excess,'-o',label = \"%.1f\"%((freq_range[np.argmax(excess)]-Photon_IF)/1e3))# label = \"%i-%i\"%(start,stop))\n",
    "PrintStatic(\"\")\n",
    "\n",
    "plt.title('average number %s \\n cycle time %.1f us \\n Spin_const_amp =  %.3f \\n amplitude_pulse = %.3f'%(average_number, cycle_time*1e3, Spin_Gauss_amplitude, amplitude_pulse))\n",
    "plt.xlabel('Frequency offset (kHz)')\n",
    "plt.ylabel('integrated excess counts')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "excess_array = np.array(excess_array)\n",
    "\n",
    "excess_array.shape\n",
    "\n",
    "################### 2D Plot ###################\n",
    "total_time_freqsweep = 642*60\n",
    "use_time = True\n",
    "chunk_index = int(chunk/2)+np.linspace(0,npts-chunk,npts//chunk,dtype = int) # Use this for y axis if you want to plot chunk centre index\n",
    "time_elapsed = np.linspace(0, total_time_freqsweep/60, len(chunk_index)).round(1)\n",
    "plt.figure()\n",
    "if use_time:\n",
    "    yaxis = time_elapsed\n",
    "    ylabel = \"Time (minutes)\"\n",
    "else:\n",
    "    yaxis = chunk_index\n",
    "    ylabel = 'Slice centre index'\n",
    "\n",
    "plot_2d_sweep(excess_array,x=np.round((freq_range-Photon_IF)/1e3,0),y=yaxis,\n",
    "              xlabel = 'Frequency offset (kHz)',ylabel = ylabel,clabel = 'integrated excess counts',xtick = 'auto',ytick = 'auto',\n",
    "            cmap = \"Blues\",horizontal_ticks = False, fontsize = None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10192d11-bf3c-4e11-8495-597f67380544",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we start processing the data by splitting the sweep in chuncks.\n",
    "# Each chunk is averaged and fit with a lorentzian function whose parameters\n",
    "# are saved is est_array. The plot is the center of the lorentzian as a function of chunk index\n",
    "\n",
    "npts = np.round(click_array.shape[0],-3)\n",
    "offset = 0\n",
    "integration_index=10\n",
    "integration_index_bg=1\n",
    "plt.figure()\n",
    "\n",
    "est_array = []\n",
    "excess_array = []\n",
    "t_step = time_hist[1]-time_hist[0]\n",
    "\n",
    "\n",
    "chunk = 200\n",
    "for start in offset+np.linspace(0,npts-chunk,npts//chunk,dtype = int):\n",
    "    PrintStatic(str(start))\n",
    "    stop = start+chunk\n",
    "    \n",
    "    x = (freq_range-Photon_IF)/1e3\n",
    "    y=(click_array[start:stop].mean(0)[:,:integration_index].sum(-1))*t_step\n",
    "\n",
    "    excess_array.append(y)\n",
    "    \n",
    "    guess = [x[np.argmax(y)],(max(x)-min(x))/4,max(y)-min(y),min(y)]\n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, y)\n",
    "    est_array.append(est)\n",
    "    \n",
    "est_array = np.array(est_array)\n",
    "excess_array = np.array(excess_array)\n",
    "plt.plot(est_array[:,0], '.')\n",
    "plt.xlabel('Chunk index')\n",
    "plt.ylabel('Fit lorentzian center (kHz)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "865de5fd-3c48-4d73-a329-123684138df9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This cell performs the classification of the states by simply setting \n",
    "# upper and lower boundries for each possible state.\n",
    "\n",
    "# To calculate the boundries we sort the center of the lorentzians and\n",
    "# observe that there are distinct regions where each lorentzian can be located\n",
    "# We can extract their boundries using a threshold in the derivative of the sorted list\n",
    "f = est_array[:,0]\n",
    "s_f = np.sort(f)\n",
    "jumps = np.diff(s_f, prepend=f[0])>1.5\n",
    "jumps_f = np.append(np.where(jumps)[0][:3], len(f))\n",
    "\n",
    "centers, ranges = [], []\n",
    "start = 0\n",
    "for end in jumps_f:\n",
    "    section = s_f[start:end]\n",
    "    centers.append(section.mean())\n",
    "    ranges.append([section.min()-0.5, section.max()+0.5])\n",
    "    start = end\n",
    "    print(ranges)\n",
    "    \n",
    "centers = np.array(centers)\n",
    "ranges = np.array(ranges)\n",
    "\n",
    "plt.plot(np.sort(est_array[:,0]), '.')\n",
    "plt.vlines(jumps_f, min(f), max(f), color='k')\n",
    "#plt.hlines(centers, 0, len(f), color='k', linestyle='dashed')\n",
    "plt.hlines(ranges[:,1], 0, len(f), color='r', linestyle='dashed')\n",
    "plt.hlines(ranges[:,0], 0, len(f), color='b', linestyle='dashed')\n",
    "plt.xlabel('Arbitrary index')\n",
    "plt.ylabel('Sorted lorentzian center (kHz)')\n",
    "\n",
    "def clasify_states(frequencies, ranges):\n",
    "    states = []\n",
    "    for f in frequencies:\n",
    "        assigned_state = -1\n",
    "        for ii, (min_range, max_range) in enumerate(ranges):\n",
    "            if min_range <= f and max_range >= f:\n",
    "                assigned_state = ii\n",
    "                break\n",
    "        states.append(assigned_state)\n",
    "    return states\n",
    "\n",
    "states = clasify_states(f, ranges)\n",
    "states_freq = [centers[i] for i in states]\n",
    "delta_freq = states_freq-f\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(f, '.')\n",
    "plt.plot(states_freq,'k.')\n",
    "plt.xlabel('Chunk index')\n",
    "plt.ylabel('Fit lorentzian center (kHz)')\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(delta_freq)\n",
    "plt.xlabel('Chunk index')\n",
    "plt.ylabel('Drift (kHz)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9494b3a8-3899-401b-b362-9c32d63f040b",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.hist(delta_freq)\n",
    "plt.ylabel('Counts')\n",
    "plt.xlabel('Drift (kHz)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e74a3b00-7177-4b79-949c-bdd10c2a3fd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This repeats the fit but shifting data using the recently calculated drift\n",
    "\n",
    "npts = np.round(click_array.shape[0],-3)\n",
    "offset = 0\n",
    "integration_index=10\n",
    "integration_index_bg=1\n",
    "\n",
    "est_array = []\n",
    "excess_array = []\n",
    "t_step = time_hist[1]-time_hist[0]\n",
    "\n",
    "cut_start = 0\n",
    "cut_end = len(freq_range)\n",
    "for ii, start in enumerate(offset+np.linspace(0,npts-chunk,npts//chunk,dtype = int)):\n",
    "    PrintStatic(str(start))\n",
    "    stop = start+chunk\n",
    "    \n",
    "    x = (freq_range-Photon_IF)/1e3\n",
    "    y = (click_array[start:stop].mean(0)[:,:integration_index].sum(-1))*t_step\n",
    "    \n",
    "    # A bit of black magic to drift the data. It is important for \n",
    "    # latter 2d plotting that all data has the same dimensions and \n",
    "    # shares a same x and so this is done here. If one doesnt want \n",
    "    # to perform the 2d plotting it would be sufficient to substract \n",
    "    # delta_freq[ii] from x. The values of 6 and -7 are arbitrary \n",
    "    # and only work for this dataset...\n",
    "    delta_ii = np.round(delta_freq[ii]/2).astype(int)\n",
    "    x = x[6:-7]\n",
    "    y = y[(6-delta_ii):(-7-delta_ii)]\n",
    "    \n",
    "    excess_array.append(y)\n",
    "    \n",
    "    guess = [x[np.argmax(y)],(max(x)-min(x))/4,max(y)-min(y),min(y)]\n",
    "    try:\n",
    "        est,std,fine,data_fit = fit_function(guess, lorentz, x, y)\n",
    "        \n",
    "    except Exception as e:\n",
    "        print('fit error, ', e)\n",
    "        \n",
    "    est_array.append(est)\n",
    "    \n",
    "excess_array = np.array(excess_array)\n",
    "est_array = np.array(est_array)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27d1b213-16fc-472c-bcea-63113fcbd067",
   "metadata": {},
   "outputs": [],
   "source": [
    "################### 2D Plot ###################\n",
    "total_time_freqsweep = 642*60\n",
    "use_time = True\n",
    "chunk_index = int(chunk/2)+np.linspace(0,npts-chunk,npts//chunk,dtype = int) # Use this for y axis if you want to plot chunk centre index\n",
    "time_elapsed = np.linspace(0, total_time_freqsweep/60, len(chunk_index)).round(1)\n",
    "plt.figure()\n",
    "if use_time:\n",
    "    yaxis = time_elapsed\n",
    "    ylabel = \"Time (minutes)\"\n",
    "else:\n",
    "    yaxis = chunk_index\n",
    "    ylabel = 'Slice centre index'\n",
    "\n",
    "X = np.round((freq_range-Photon_IF)/1e3,0)[6:-7]\n",
    "plot_2d_sweep(excess_array,x=X,y=yaxis,\n",
    "              xlabel = 'Frequency offset (kHz)',ylabel = ylabel,clabel = 'integrated excess counts',xtick = 'auto',ytick = 'auto',\n",
    "            cmap = \"Blues\",horizontal_ticks = False, fontsize = None)\n",
    "\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "fit_freqs = []\n",
    "states = np.array(states)\n",
    "for i in range(4):\n",
    "    y = excess_array[states==i].mean(0)\n",
    "    guess = [X[np.argmax(y)],(max(X)-min(X))/4,max(y)-min(y),min(y)]\n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, y)\n",
    "    plt.plot(X, y, 'o', color=colors[i])\n",
    "    plt.plot(fine, data_fit, color=colors[i], label=f'{labels[i]} | f = {est[0]:.1f} kHz | $\\delta$ = {est[1]:.1f} kHz')\n",
    "    fit_freqs.append(est[0])\n",
    "\n",
    "A1 = ((fit_freqs[2]-fit_freqs[0]) + (fit_freqs[3]-fit_freqs[1]))/2\n",
    "A2 = ((fit_freqs[1]-fit_freqs[0]) + (fit_freqs[3]-fit_freqs[2]))/2\n",
    "plt.legend()\n",
    "plt.ylim([0.15, 0.6])\n",
    "plt.xlabel('Frequency offset (kHz)')\n",
    "plt.ylabel('Count rate (/ms)')\n",
    "plt.title(f'$A_1$ = {A1:.1f} kHz | $A_2$ = {A2:.1f} kHz ')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18ed46ea-0b83-4156-a9d9-e5eb94888430",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-15T16:36:51.707615Z",
     "iopub.status.busy": "2023-12-15T16:36:51.707615Z",
     "iopub.status.idle": "2023-12-15T16:36:51.727612Z",
     "shell.execute_reply": "2023-12-15T16:36:51.727612Z",
     "shell.execute_reply.started": "2023-12-15T16:36:51.707615Z"
    },
    "toc-hr-collapsed": true
   },
   "source": [
    "# Preparation and readout"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6711159-93e7-4411-99b5-2fc8ee74e71a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-10T11:24:15.643778Z",
     "iopub.status.busy": "2024-02-10T11:24:15.643778Z",
     "iopub.status.idle": "2024-02-10T11:24:15.660781Z",
     "shell.execute_reply": "2024-02-10T11:24:15.659778Z",
     "shell.execute_reply.started": "2024-02-10T11:24:15.643778Z"
    }
   },
   "source": [
    "## Chirped vs duration and amplitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08a3ed50-b311-47cd-ae07-a18cf160a9c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1400 kHz high power, large range, b flipped\n",
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\nuclear_4stateprep\\\\'\n",
    "#timestamp_list = grab_timestamps_hdf(path, \"20240209232821\", \"20240210113820\")\n",
    "#timestamp_list = grab_timestamps_hdf(path, \"20240210134001\", \"20240210151124\")\n",
    "timestamp_list = grab_timestamps_hdf(path, \"20240212214917\",\"20240213084557\")\n",
    "data_list = load_from_directory(path, timestamp_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fc749ba-a8ea-442d-bf81-aae3d89445d7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "#chirped_pump_amplitudes = np.linspace(0.01,0.3,5)\n",
    "#chirped_pump_durations = np.linspace(300e3,10e6,5)\n",
    "#chirped_pump_amplitudes = np.linspace(0.2,0.3,2)\n",
    "#chirped_pump_durations = np.linspace(5e6,10e6,2)\n",
    "chirped_pump_amplitudes = np.linspace(0.05,0.15,5)\n",
    "chirped_pump_durations = np.linspace(1e6,11e6,5)\n",
    "\n",
    "idx=0\n",
    "probs_sweep = []\n",
    "for chirped_pump_amplitude in chirped_pump_amplitudes:\n",
    "    probs_inner = []\n",
    "    for chirped_pump_duration in chirped_pump_durations:\n",
    "        data = data_list[idx][0][Key_field]['click_array']\n",
    "        N_ROcycle_array = data_list[i][0][Key_field]['x']\n",
    "        x = N_ROcycle_array\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "        ############################### extract populations #################################\n",
    "        pops = []\n",
    "        deltas = []\n",
    "        for i in range(len(N_ROcycle_array)):\n",
    "            data_nro = data[:,i]\n",
    "            p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data_nro, frequency_domain = True, accumulated=True)#, kmeans = kmeans)\n",
    "            pops.append([p0,p1,p2,p3])\n",
    "            deltas.append([delta_x,delta_y])\n",
    "\n",
    "        pops = np.array(pops)\n",
    "        ############################### Plot raw histograms ##############################\n",
    "\n",
    "        for k in range(len(N_ROcycle_array)):\n",
    "            plt.figure()\n",
    "            \n",
    "            for j in range(4):\n",
    "                plt.subplot(2,2,j+1)\n",
    "                for i  in range(4): plt.hist(data[:,k,j,i], alpha = 0.5,bins = 40, range = (0,200))\n",
    "                plt.xlabel(\"SMPD counts\")\n",
    "                plt.ylabel(\"counts\")\n",
    "                plt.title(\"Preparing \"+labels[j], color=colors[j])\n",
    "            plt.tight_layout()\n",
    "\n",
    "        ############################### Plot clouds ###############################\n",
    "        rows = int(np.floor(len(x)/5.01)+1)\n",
    "        if len(x)<5:columns = (len(x))//rows\n",
    "        else: columns = 5\n",
    "        fig, axs = plt.subplots(rows,columns,figsize = (2+columns*3,3+rows*2), tight_layout=True)\n",
    "        axs = np.array([axs]).flatten()\n",
    "        fig.suptitle(\"K-means\")  \n",
    "\n",
    "        probs = []\n",
    "        for i, delta in enumerate(deltas):\n",
    "            prob, kmeans = kmeans_plot(delta, h=3, ax=axs[i], title=f'Am: {chirped_pump_amplitude:.2}, duration {chirped_pump_duration*4e-6:.0f} ms')\n",
    "            probs.append(prob)\n",
    "        probs = np.array(probs)\n",
    "        probs_inner.append(probs)\n",
    "\n",
    "\n",
    "        ##############################################################  \n",
    "        fig,ax=plt.subplots(2,2,figsize=(10,8), tight_layout=True)\n",
    "        ax = ax.flatten()\n",
    "        # fig.suptitle(\"K-means Readout vs N_RO\")  \n",
    "        if len(N_ROcycle_array)>1:\n",
    "            for i in range(4): \n",
    "                for j in range(len(readout_freqs)): ax[i].plot(x, probs[:,i,j], \"o-\", label = labels[j])\n",
    "                ax[i].set_ylabel('Probability')\n",
    "                ax[i].set_xlabel('Number of readout pulses')\n",
    "                ax[i].legend()\n",
    "                ax[i].set_title(\"Preparing \"+labels[i], color=colors[i])\n",
    "                ax[i].set_ylim([0,1])\n",
    "                ax[i].grid(True)\n",
    "        else:\n",
    "            for i in range(4):\n",
    "                ax[i].set_title(\"Preparing \"+labels[i], color=colors[i])\n",
    "                bar = ax[i].bar(labels,probs[:,i,:].flatten().round(3), color = colors, alpha = 0.7, label = probs[:,i,:].flatten().round(3))\n",
    "                ax[i].set_ylim([0,1])\n",
    "                ax[i].bar_label(bar)\n",
    "                ax[i].set_ylabel(\"$P_N$\")\n",
    "\n",
    "        idx+=1\n",
    "    probs_sweep.append(probs_inner)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80b77e47-cd60-4f07-8ca9-771d8efa3c39",
   "metadata": {},
   "outputs": [],
   "source": [
    "import importlib\n",
    "importlib.reload(sns)\n",
    "importlib.reload(plt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35771fa8-ed70-476a-a784-aadc9afe963f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.reset_defaults()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c193e7a8-3d72-4284-be7f-f0088f52a29c",
   "metadata": {},
   "outputs": [],
   "source": [
    "probs_sweep=np.array(probs_sweep)\n",
    "\n",
    "cmaps = ['Blues', 'Oranges', 'Greens', 'Reds']\n",
    "fig, axs = plt.subplots(2,2,tight_layout=True,figsize=(10,8))\n",
    "axs = axs.flatten()\n",
    "for i in range(4):\n",
    "    plt.sca(axs[i])\n",
    "    plot_2d_sweep(\n",
    "        probs_sweep[:,:,0,i,i],\n",
    "        x = np.round(chirped_pump_durations*1e-6, 1),\n",
    "        y = np.round(chirped_pump_amplitudes, 2),\n",
    "        xlabel = r\"Pulse Duration (ms)\",\n",
    "        ylabel = r\"Pulse amplitude (a.u.)\",\n",
    "        clabel = \"Fidelity\",\n",
    "        cmap = cmaps[i],\n",
    "        horizontal_ticks=True,\n",
    "        vmin=0,\n",
    "        vmax=1,\n",
    "        annot=True\n",
    "    )\n",
    "    axs[i].set_title('Preparing '+labels[i], color=colors[i])\n",
    "\n",
    "probs_avg = np.mean([probs_sweep[:,:,0,i,i] for i in range(4)], axis=0)\n",
    "plt.figure()\n",
    "plot_2d_sweep(\n",
    "    probs_avg,\n",
    "    x = np.round(chirped_pump_durations*1e-6, 1),\n",
    "    y = np.round(chirped_pump_amplitudes, 2),\n",
    "    xlabel = r\"Pulse Duration (ms)\",\n",
    "    ylabel = r\"Pulse amplitude (a.u.)\",\n",
    "    clabel = \"Fidelity\",\n",
    "    cmap = 'Greys',\n",
    "    horizontal_ticks=True,\n",
    "    vmin=0.8,\n",
    "    vmax=1,\n",
    "    annot=True,\n",
    "    title='Mean preparation fidelity'\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fb840a7-4d62-4faa-b34b-6813c853518c",
   "metadata": {},
   "source": [
    "# Chirped preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed675c7c-40d4-4467-8ec7-5998a15f4dff",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\nuclear_red_prep_vs_pumping_steps_chirped\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = [\n",
    "    \"20240222114100\",\"20240222130155\"\n",
    "    \n",
    "]\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    filenames=getfiles_hdf5(path)\n",
    "    for filename in filenames:\n",
    "        if timestamp in filename: \n",
    "            print('Loading '+path+filename)\n",
    "            data=load_h5_to_dic(path+filename)\n",
    "            data_list.append(data)\n",
    "    \n",
    "def lorentz(delta,delta0,kappa,a,b):\n",
    "    return a/(1+(delta-delta0)**2/(kappa/2)**2)+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d70f44e4-c815-46e0-96d2-47c4031be569",
   "metadata": {},
   "outputs": [],
   "source": [
    "###########################################################\n",
    "Key_field='0'\n",
    "data0 = data_list[1][0][Key_field]['click_array']\n",
    "data1 = data_list[0][0][Key_field]['click_array']\n",
    "data = np.concatenate((data0, data1))\n",
    "x = data_list[0][0][Key_field]['x']\n",
    "readout_freqs = data_list[0][0][Key_field]['readout_freqs']\n",
    "print(data.shape)\n",
    "print(x)\n",
    "\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "############################### extract populations #################################\n",
    "pops = []\n",
    "deltas = []\n",
    "for i in range(len(x)):\n",
    "    data_nro = data[:,i]\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data_nro, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops.append([p0,p1,p2,p3])\n",
    "    deltas.append([delta_x,delta_y])\n",
    "\n",
    "probs = np.array(pops)\n",
    "\n",
    "##############################################################  \n",
    "fig,ax=plt.subplots(1,1,figsize=(5,5), tight_layout=True)\n",
    "# fig.suptitle(\"K-means Readout vs N_RO\")  \n",
    "if len(x)>1:\n",
    "    for j in range(len(readout_freqs)): ax.semilogy(x, 1-probs[:,j], \"o-\", label = labels[j])\n",
    "    ax.set_ylabel('1-Probability')\n",
    "    ax.set_xlabel('Number of readout pulses')\n",
    "    ax.legend()\n",
    "    ax.set_title(\"Preparing \"+labels[-1], color=colors[-1])\n",
    "    ax.set_ylim([1e-2,1])\n",
    "    ax.grid(True)\n",
    "else:\n",
    "    ax.set_title(\"Preparing \"+labels[-1], color=colors[-1])\n",
    "    bar = ax[i].bar(labels,probs[:,:].flatten().round(3), color = colors, alpha = 0.7, label = probs[:,:].flatten().round(3))\n",
    "    ax.set_ylim([0,1])\n",
    "    ax.bar_label(bar)\n",
    "    ax.set_ylabel(\"$P_N$\")\n",
    "\n",
    "\n",
    "############################### Plot raw histograms ##############################\n",
    "\n",
    "for k in range(len(x)):\n",
    "    plt.figure(figsize=(5,5))\n",
    "    for i  in range(4): plt.hist(data[:,k,i], alpha = 0.5,bins = 40, range = (0,200))\n",
    "    plt.xlabel(\"SMPD counts\")\n",
    "    plt.ylabel(\"counts\")\n",
    "    plt.title(\"Preparing \"+labels[-1], color=colors[-1])\n",
    "    plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06a5bae6-db05-4ef2-bd16-9a7bc9f810bc",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Raman Rabi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b906c88e-ded6-42aa-8d74-045023afcd4d",
   "metadata": {},
   "source": [
    "## Spin A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "02579fda-64f1-4f7c-8732-ac51ce0bbbef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-31T14:50:13.022775Z",
     "iopub.status.busy": "2024-03-31T14:50:13.021775Z",
     "iopub.status.idle": "2024-03-31T14:50:18.315674Z",
     "shell.execute_reply": "2024-03-31T14:50:18.314673Z",
     "shell.execute_reply.started": "2024-03-31T14:50:13.022775Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape: (450, 21, 4)                                                      \n"
     ]
    }
   ],
   "source": [
    "path='Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_rabi_a'\n",
    "timestamp_list = [\"20240331120942\"]#grab_timestamps_hdf(path, \"20240318145645\",\"20240318145645\")\n",
    "data_list = load_from_directory(path, timestamp_list)\n",
    "\n",
    "Key_field = '0'\n",
    "data = data_list[0][0][Key_field]['click_array']\n",
    "raman_pulse_durations = data_list[0][0][Key_field]['raman_pulse_durations']\n",
    "delta_freqs = 1e-3*data_list[0][0][Key_field]['delta_freqs']\n",
    "print('Data shape:', data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "988b22c0-d33e-4501-a3f9-26c6ecf848ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-31T14:50:19.897288Z",
     "iopub.status.busy": "2024-03-31T14:50:19.896288Z",
     "iopub.status.idle": "2024-03-31T14:50:22.326009Z",
     "shell.execute_reply": "2024-03-31T14:50:22.325007Z",
     "shell.execute_reply.started": "2024-03-31T14:50:19.897288Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(450, 21, 4)\n",
      "[0.10480651041273642, 100, 0.4, 0.9, 3.141592653589793]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x2500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_guess = 1\n",
    "###########################################################\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "\n",
    "############################### Fit #################################\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(pops[-1] - pops[-1].mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    \n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([x[0],x[-1]])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            #guess = [1e-3*rabi_freq,30,(-1)**(i//2+1)*0.5,(-1)**(i//2+1),0.9-i*np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            \n",
    "            guess = [(2*4.43)**-1,30,(-1)**(i//2+1)*0.5,(-1)**(i//2+1),0.9-i*np.pi]\n",
    "            # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "            #ax[1].plot(fine,rabi_fit(fine,*guess)+0.05*i, color = colors[i],alpha = 0.5)\n",
    "\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==1: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "\n",
    "    \n",
    "# if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "try: \n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except: pass\n",
    "#ax[1].set_ylim(0,1)\n",
    "#ax[1].set_xlim(2.5,3.6)#x[0],x[-1])\n",
    "ax[1].set_xlim(x[0],x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()\n",
    "\n",
    "bins=np.arange(20,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(delta_freqs)\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (Hz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} Hz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "save_fig_manustyle(path+\"\\\\\"+timestamp_list[0]+\"_raman_rabi_a_nuclear_spin.pdf\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "e0642ea2-8292-417a-89e9-c968afbca312",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-31T14:54:07.426878Z",
     "iopub.status.busy": "2024-03-31T14:54:07.426878Z",
     "iopub.status.idle": "2024-03-31T14:54:07.574880Z",
     "shell.execute_reply": "2024-03-31T14:54:07.572879Z",
     "shell.execute_reply.started": "2024-03-31T14:54:07.426878Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x120208ed848>"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sJivXZnQsEREpx1RQpES83N2YNKQrNSp5cSj1LK8v2EBhobbDFxGRsqGCIiUWWMWHSUNuxMNiZnVsMl+u2mt0JBERKadUUMQhzUKqM+a2dgB8FrNH2+GLiEiZUEERh93augF3tG8EwGsLNnDk1DmDE4mISHmjgiJX5Zm+rWhZtwZZuTaen7NGi2ZFRKRUqaDIVXF3s/DmnV2oUdmbpFPnmPD9Ri2aFRGRUqOCIletemVv3hrcBXeLmZUHjjFrzT6jI4mISDmhgiLXJKJODUb3awvAp8t3s/7gcYMTiYhIeaCCItfsn21Dub1tKHbg1fnrOXo60+hIIiLi4lRQpFSMvKUNESHVycyx8cJ3a7Bq0ayIiFwDFRQpFR5uFiYO7kr1P3aafePHTdjtWjQrIiJX56oKSmJiIqNHj6ZHjx60aNGCPn36MGXKFLKyshyeKysriw8//JDbbruNli1b0rp1a+69916WLFlyNdHEQAFVvJk4uCtuZjMr9h3l67UHjI4kIiIuyuGCsmvXLgYOHMjChQsJCAggKioKq9XKjBkzGDJkCJmZJV9/kJqayp133sm0adNIT0+na9euhIeHs2XLFp566ilmz57taDwxWIu6NRh5SxsAPl62i22JJw1OJCIirsihgmKz2RgxYgRWq5VJkyYxd+5cpk6dytKlS+nZsydxcXG89957JZ7v5ZdfJiEhgVtuuYXly5czffp05syZw+eff467uzuTJk3ixIkTDv9QYqwB7ULp17I+hXY7L0evI+1cttGRRETExThUUBYtWkRycjJdunRhwIABRce9vLyYOHEiPj4+REdHc+7clbc+37VrFytXrqRevXq8/fbbeHp6Fo117dqVAQMGEBgYyM6dOx2JKE7AZDLx3K3taFizGulZubw8by35BYVGxxIRERfiUEFZsWIFAH369PnLmJ+fH5GRkdhsNtasWXPFuX799VcAHnjgATw8PP4yPmHCBFasWEHfvn0diShOwsvDjYmDu+Dr6c6uo6eY/ruKpoiIlJxDBSUuLg6A8PDwYscbNbrwALnY2NgrzrVnzx4AWrVqhdVq5fvvv2f8+PG8+uqrREdHk5ub60g0cUJ1qldm3IBIAOZsiGXZ3iMGJxIREVfh5sjJJ09eWPBYs2bNYscDAgKAC4tfryQpKQmA06dP89RTT5GcnFw0NmfOHGbMmMEnn3xCaGioIxHFyXRrHMLQLk2YvXY/E3/cRGhgNeoHVDE6loiIODmHCkp29oXFjl5eXsWOXzxutVqvONf58+cBGDVqFCEhIUyePJkmTZpw7Ngx3n33XVavXs3DDz/MTz/9RKVKlYqdIyUlhV69ev3t91i2bNkVc0jZe6Rnc/Ymn2ZbUiovzV3DZ8NvwsfT3ehYIiJSRi733pySkkJwcPAV53DoIx6LxVKi80qyQdfFj3C8vLyYNWsW7du3p1KlSjRu3JgZM2YQFhZGcnIy0dHRjkQUJ+RmMTNhUGdqVPIiMe0ckxZu1iZuIiJyWQ5dQfH19SUjI+Nv14fk5OQA4OPjc8W5vL29OX/+PAMHDqRy5cqXhnJzY8iQIYwfP57169fz4IMPFjtHcHCwrpK4CP9KXrwxuAtP/Hc5v+85QvM6NbgzMszoWCIiUgYu9958uasrf+bQFZTAwEAA0tLSih2/uPbk4nmXU716dQBCQkKKHb94/MyZM45EFCfWsm4AT97UCoCpi3ew++gpYwOJiIjTcqigXLx75+DBg8WOx8fHX3JeSea6uPD2f10sQReLjJQPd3UMo2fTOuQXFjJ27lrSs3KMjiQiIk7IoYISFRUFUOxzctLT09m4cSOenp506tSpxHMtWrSI/Pz8v4yvWrUKgA4dOjgSUZycyWTipf4dqFu9MmmZ2YyLXk9BoTZxExGRSzlUUHr37k3t2rWJiYlhzpw5RcdzcnIYO3YsVquVwYMH4+/vXzRms9lISEggISEBm81WdLxfv36EhIRw6NAhJkyYcElJmTdvHosXL6ZatWrcfvvt1/DjiTPy9XRn0l1d8XZ3Y0viSb5YudfoSCIi4mRMdgdvp9i8eTPDhw8nJyeHZs2aERISwvbt20lNTSUiIoJZs2bh6+tbdP6xY8eKFsQsW7bskjUne/bsYfjw4aSnp1OzZk1atGjB4cOHiYuLw8vLiw8++KDoSsv/+vOc4poW70ritQUbMAH/NzSKDqFBZfa9svPy6Tnxwh1hy18ahLeHQ+vDRUSklJT0/dvhpxm3b9+eefPm0bdvX44fP05MTAyVK1fmySefZObMmZeUkyuJiIhg4cKFDB06FA8PD2JiYkhPT+e2225j7ty5f1tOpHzo26I+/ds0wA68Nn+9HiooIiJFHL6C4ix0BaV8yLHl88hnSzl4MoNW9QKYdn8P3CwO9+Yr0hUUERHnUGZXUERKk5e7G2/c2RkfDzd2HE7js5g9RkcSEREnoIIihqtbowov/vPC3VozV+9j/cEUgxOJiIjRVFDEKfSOqMvAdg0BGP/9BlLPXvl5TiIiUn6poIjTeLpva8KC/Miw5jJu/jryC7Q/iohIRaWCIk7D091StB5l55FTfLp8t9GRRETEICoo4lTqVK/M2P6RAMxeu5+1cccNTiQiIkZQQRGn07NZHQZ1aARcWI9yIiPL4EQiInK9qaCIU3qqTyua1PLnXHYer0RrPYqISEWjgiJOycPtwnqUSp7u7Dl2mo+W7jQ6koiIXEcqKOK0avlVYuztF9ajfLs+ltUHkg1OJCIi14sKiji1qCYh3NUxDIAJP2zUehQRkQpCBUWc3hO9W9K0tj+ZOXmMm79e61FERCoAFRRxeu5uFsbf0RlfT3d2Hz2l5/WIiFQAKijiEmr7V+KFf7QHYNbqfWxKOGFwIhERKUsqKOIyekfUpX/bUOzA6ws2cOZ8jtGRRESkjKigiEsZcXNrGgRW5UxWDuO/30Bhod3oSCIiUgZUUMSleLm78cagzni6WdiYcIKv1u43OpKIiJQBFRRxOTcEVuXZfm0A+HT5bnYfPWVwIhERKW0qKOKS/tG6ATdF1KXAbmdc9DrOZecZHUlEREqRCoq4JJPJxPO3tae2XyVOnLUy8cdN2O1ajyIiUl6ooIjL8vVyZ8KgzriZzaw8cIwFm+ONjiQiIqVEBUVcWpPa/vz7ppYATF28nbiUdIMTiYhIaVBBEZc3pGMYXcJqkVdQyCvR67Dm2oyOJCIi10gFRVyeyWTi5f6RBFT25sjpTN77ZavRkURE5BqpoEi5UM3Xk9fv6ITZZOKXnUn8ujPR6EgiInINVFCk3GhdP5B/dW8GwDs/b+XwqXMGJxIRkaulgiLlyoPdmtKmfiDZtnxeiV5Hrq3A6EgiInIVVFCkXLGYzbw2sBPVfDw5eCKDD3/fYXQkERG5CiooUu4EVPFm3IBIAKI3HWR1bLLBiURExFEqKFIudWpUiyEdwwF484eNpGVaDU4kIiKOUEGRcuvx3i0IC/LjbHYeb/242eg4IiLiABUUKbc83CyMH9QJL3cLO46kGR1HREQc4GZ0gGtht0N2Xr7RMcSJBVbx4cmbWvHunzZv058ZERHj2O1gMl35PJcuKCkZWfScGG10DHExt777g9ERREQqrIyMLGr5+V7xPH3EIyIiIk7Hpa+gBFfzZdFLg4yOIS4gOy//kisnY25rx80t6huWR0Skorp11X9KdJ5LFxSTCbw9XPpHEINMW7yDtvUDqVujitFRREQqlJKsPwF9xCMVUMu6AWTb8nl1/nps+doKX0TEGamgSIXz0j/bU8XbgwMp6cxYvtvoOCIiUgwVFKlwAqr4MLZ/BwC+WXeADfEpBicSEZH/pYIiFVK3xiEMbN8QgAnfb+TM+RyDE4mIyJ+poEiF9VSfVjQIrMqZrBwm/LCRwkK70ZFEROQPKihSYXm5uzH+jk54uFnYEJ/CdxvjjI4kIiJ/UEGRCi20ZjWe6dsagI9+30ns8TMGJxIREVBBEWFAu1C6Na5NfmEhr0Svx5prMzqSiEiFp4IiFZ7JZOKlf3YgsIo3R89k8v6v24yOJCJS4amgiABVfTx5dWAnTMCiHYn8vvuw0ZFERCo0FRSRP7SpH8iD3ZoBMPnnLRxPP29wIhGRiksFReRP/tW9Gc3r1CAr18ar89eTX1BodCQRkQpJBUXkT9wsZl4f2JFKnu7sOXaaz2P2GB1JRKRCUkER+R/BfpV4/h/tAZi5eh9bE08anEhEpOJRQREpRu+IuvyjdQPswGsLNpCRlWt0JBGRCkUFReRvjLylDXWrV+ZUZjYTf9qE3a6t8EVErhcVFJG/4e3hxvhBnXG3mFkdm8z8zfFGRxIRqTBUUEQuIzzYjyduagnAtMXbiT+ZYWwgEZEKQgVF5AoGR4bRuVEt8goKGRe9jpy8fKMjiYiUeyooIldgMpl4+fYOVK/kRWLaOT5YvN3oSCIi5Z4KikgJ+Pl6MW5AR0zAD1sTWLHvqNGRRETKNRUUkRLqEBrEfV2aAPDWT5s4kZFlcCIRkfJLBUXEAY/0bE7T2v5k5th4fcEGbYUvIlJGVFBEHOBmMfP6HZ3w8XBjx5E0Zq7eZ3QkEZFySQVFxEEh/pUZc1s7AL5YuZcdh1MNTiQiUv6ooIhchb4t6nNLy/oU2u28Nn8DZ63aCl9EpDSpoIhcpVH92hLiX4mT56xMWrhZW+GLiJQiFRSRq+Tr6c6EQZ1xM5uJ2X+MH7YmGB1JRKTcUEERuQaNa/nzeO8WAPzfb9s5lHrW4EQiIuWDCorINRrSMZzI0CDy8gsubIVv01b4IiLXSgVF5BqZzSbGDeiIn68nCaln+XDJTqMjiYi4PBUUkVLgX+nCVvgA8zcfZNWBYwYnEhFxbSooIqWkY8Ng7u4UDsCbP24i9azV4EQiIq5LBUWkFD3eqwWNg/04l53HawvWU1CorfBFRK7GVRWUxMRERo8eTY8ePWjRogV9+vRhypQpZGVd+8PTJk+eTHh4ONOmTbvmuUSuN3c3C+MHdcbb3Y3th9OYtXq/0ZFERFySwwVl165dDBw4kIULFxIQEEBUVBRWq5UZM2YwZMgQMjMzrzrM2rVr+fLLL6/660WcQZ3qlRl9a1sAPo/Zw64jpwxOJCLiehwqKDabjREjRmC1Wpk0aRJz585l6tSpLF26lJ49exIXF8d77713VUHOnDnD888/r904pVy4pWV9+javR4Hdzqvz15GZnWd0JBERl+JQQVm0aBHJycl06dKFAQMGFB338vJi4sSJ+Pj4EB0dzblz5xwO8tJLL5Genk6bNm0c/loRZ2MymXju1nbU9vPlxFlthS8i4iiHCsqKFSsA6NOnz1/G/Pz8iIyMxGazsWbNGodCfP3116xYsYInnniCiIgIh75WxFn5ernz+h2dsZhNLN93lIXbDxkdSUTEZThUUOLi4gAIDw8vdrxRo0YAxMbGlnjOgwcPMnnyZNq0acOjjz7qSBwRp9cspDqP9rywFf6UX7eRlKat8EVESsLNkZNPnjwJQM2aNYsdDwgIACA1NbVE8+Xm5vLss8/i7u7OO++8g8VicSQOKSkp9OrV62/Hly1b5tB8ImXh3s6N2XzoBJsPnWRc9Hr+M/wmPN0d+7MuIuJKLvfenJKSQnBw8BXncOgKSnZ2NnBhzUlxLh63Wku2QdXbb79NXFwcr7zyCiEhIY5EEXEZF7fCr+bjycGTGXy0VFvhi4hciUNXUCwWC4Ul2HiqJIsBY2Ji+Oqrr+jXrx+33367IzGKBAcH6yqJuIQalb155fZIRn2zirkb42jfoCZdw2sbHUtEpExc7r35cldX/syhKyi+vr7AhY9mipOTkwOAj4/PZedJS0vjxRdfJDg4mNdff92RCCIuq3NYLe7qGAZc2Ao/7Vy2wYlERJyXQ1dQAgMDycjIIC0trdjPjy6uPQkMDLzsPB9//DFnzpyhSZMmjB8//pKxvXv3ArBkyRIOHz5MaGgojz/+uCMxRZzWv3u3ZFtSKgdPZPD69+v5YGgUFrOeOCEi8r8cKijh4eHExcVx8OBBWrRo8Zfx+Pj4ovMu5+Ialf3797N/f/FbgcfFxREXF0eHDh1UUKTc8HCzMP6Ozgz7dDFbE1P5eu0B7r+xqdGxREScjkO/ukVFRQEXrm78r/T0dDZu3IinpyedOnW67DyTJk0iNja22H/uv/9+AJ588kliY2OZPXu2IxFFnF79gCo8e8uFrfA/Xb6bvcdOG5xIRMT5OFRQevfuTe3atYmJiWHOnDlFx3Nychg7dixWq5XBgwfj7+9fNGaz2UhISCAhIQGbzVZ6yUVc2G2tb6B3s7oU2O2Mi17H+RxthS8i8mcOFRQvLy8mT56Ml5cXr776KgMHDuTpp5/mpptuYtmyZURERDBy5MhLvubkyZP069ePfv36Fe2jIlLRmUwmxtzWjuBqvhzPyOLtn7doK3wRkT9xeHVe+/btmTdvHn379uX48ePExMRQuXJlnnzySWbOnFl0p4+IXF5lbw9ev6MTFpOJ3/cc4ZedSUZHEhFxGia7i/7advE+au2DIiWRnZdPz4nRACx/aRDeHg6tDy9T/121l0+W78bb3Y3/PtqHujWqGB1JRKTMlPT9W/c3ihhsaNcmtKkfSLYtn5ej15FrKzA6koiI4VRQRAxmMZt5bWCnC1vhn8hg2pIdRkcSETGcCoqIEwio4s24AZEAzN98kBX7jhqcSETEWCooIk6iU6Na3NelMQATf9zE8fTzBicSETGOCoqIE3m0ZwsiQqpzPtfGK9HrsOVrPYqIVEwqKCJOxM1iZvygzlT2cmdf8hk+XrbL6EgiIoZQQRFxMsHVfBnb/8J6lG/Xx7ImNtngRCIi158KiogT6t4khMGRYQBM+GEjqWetBicSEbm+VFBEnNQTN7WkcbAf57LzGDd/HfkFhUZHEhG5blRQRJyUh5uFCXd2xsfDjZ1HTvF5zB6jI4mIXDcqKCJOLMS/Mi/+swMAM1fvY1PCCYMTiYhcHyooIk6ud0Rdbm8bih14bcF6TmdmGx1JRKTMqaCIuIBnbm5NaGBV0rNyeXXBegoKtR5FRMo3FRQRF+Dl7sYbd3bBy93C1sRUZq3eb3QkEZEypYIi4iLqB1ThuVvbAfBZzB62J6UanEhEpOyooIi4kH6tbqBfy/oU2u2Mm7+ejKxcoyOJiJQJFRQRFzOqX1vq1ajCqcxsJvywgcJCu9GRRERKnQqKiIvx8XTnjUGd8XCzsO5gCl+vO2B0JBGRUqeCIuKCGgZV49lb2gDwybJdWo8iIuWOCoqIi/pnmwbc3KI+BXY746LXceZ8jtGRRERKjQqKiIsymUyMua0dDQKqcup8DuPmr9P+KCJSbqigiLgwbw833hzcGW93N7YmpvJ5zF6jI4mIlAoVFBEXVz+gKs//48L+KP9dtZcN8SkGJxIRuXYqKCLlQN8W9RnQruGF5/XMX8/Js1lGRxIRuSYqKCLlxDN9WxMe7MfZ7DxenreO/AKtRxER16WCIlJOeLpbePPOLlTydGfPsdNM/32n0ZFERK6aCopIOVLbvxKvDIgEYM6GWGL2HTU4kYjI1VFBESlnujUO4Z7OjQF448dNHD2daXAiERHHqaCIlEOP92pBy7o1yMq1MXbeWnJs+UZHEhFxiAqKSDnkZjEzflBn/Hw8OXgig//7dZvRkUREHKKCIlJOBVbx4bU7OmECftx2iF93JhodSUSkxFRQRMqxDqFBPBQVAcDkhVuIP5FhbCARkRJSQREp5x7s1pTI0CBy8wt44bs1ZGbnGR1JROSKVFBEyjmL2czrd3QiqKoPyennef37DRQW2o2OJSJyWSooIhVAVR9PJt3VFQ+LmbVxx/nvaj1UUEScmwqKSAURXsuf52678FDBz1bsYf3B4wYnEhH5eyooIhXIba0bcHvbUOzAq/M3cDz9vNGRRESKpYIiUsGMvKUNTWv7k5mTxwvfrdEmbiLilFRQRCoYDzcLEwd3KdrE7Z2ft2C3a9GsiDgXFRSRCqhmVV/GD+qM2WTil51JfL8l3uhIIiKXUEERqaDaNajJ471bADDl1+3sPnrK4EQiIv+fCopIBXZv58b0aBJCfmEhL81dy5nzOUZHEhEBVFBEKjSTycTY2yOpV6MKpzKzeSV6HfkFhUbHEhFRQRGp6Hw93Zl0V1d8PNzYlpTK9N93Gh1JREQFRUSgfkAVXrk9EoA5G2L5ZYeefCwixlJBEREAoprW4cFuTQGYvHAz+5JPG5xIRCoyFRQRKfJwVHO6htUir6CQF+as4XRmttGRRKSCUkERkSJms4nXBnaifo0qpGVm8+LcteTlFxgdS0QqIBUUEbmEr5c7k4d0pZKnO7uPnuK9X7Zqp1kRue5UUETkL+rWqFK00+xP2w6xYLN2mhWR60sFRUSK1alRMI/3+mOn2d+2sS0p1eBEIlKRqKCIyN+6t0tjboqoS0GhnbFz15KSkWV0JBGpIFRQRORvmUwmXvpnB8KC/Miw5vL8nNXk5OUbHUtEKgAVFBG5LC8PNyYP6YqfjycHT2Tw5o+btGhWRMqcCoqIXFFQNV/eHNwFi9nE0r1HmLl6n9GRRKScU0ERkRJpXT+QUf3aAvDJ8t0s33vU4EQiUp6poIhIiQ1o15DBkWEAjP9+A/uTzxicSETKKxUUEXHIU31a0bFhMLn5BYyZs5rUc1ajI4lIOaSCIiIOcbOYmTCoEzcEVOFUZjbPf6s7e0Sk9KmgiIjDKnl58O493ajm48mBlHTGf7+BwkLd2SMipUcFRUSuSi2/Sky6qyvuFjMr9h/jPyt2Gx1JRMoRFRQRuWot6wXw/D/aA/Df1fv4bWeSsYFEpNxQQRGRa3JrqxsY2qUJABN/2sTuo6cMTiQi5YEKiohcs8d6taBb49rYCgp5fs5qUtLPGx1JRFycCoqIXDOz2cSrAzrSKKga6Vm5PPv1Ks5l5xkdS0RcmAqKiJQKH0933r27GwGVvUk6dY4Xv1tDXn6B0bFExEWpoIhIqQms6sP793bHx8ONbUmpTNSDBUXkKqmgiEipahhUjYmDu2AxmVi8+zCfLtftxyLiOBUUESl1kQ2DL7n9+KetCQYnEhFXo4IiImXiH20aMKxbMwDe/nkL6w+mGJxIRFzJVRWUxMRERo8eTY8ePWjRogV9+vRhypQpZGVlOTxXTEwMw4cPp2PHjkRERNClSxeefvppdu3adTXRRMSJPNwjgptb1KfAbufleWuJS0k3OpKIuAiHC8quXbsYOHAgCxcuJCAggKioKKxWKzNmzGDIkCFkZmaWeK7333+fRx99lDVr1lC7dm26d+9OlSpVWLx4MXfffTc//PCDo/FExImYTCZe+md72tYPxJqXz6hvVnHyrOO/yIhIxeNQQbHZbIwYMQKr1cqkSZOYO3cuU6dOZenSpfTs2ZO4uDjee++9Es21ZcsWPvnkE3x8fPjqq6+YP38+06dP59dff+X1118nPz+fcePGceLEiav6wUTEObi7WXjrrq5FTz9+9utVZGqPFBG5AocKyqJFi0hOTqZLly4MGDCg6LiXlxcTJ07Ex8eH6Ohozp07d8W5oqOjARg+fDjt2rW7ZGzIkCF0796d3NxcFi9e7EhEEXFClb09eO/e7lSv5MWh1LOM+XY1ObZ8o2OJiBNzqKCsWLECgD59+vxlzM/Pj8jISGw2G2vWrLniXF5eXoSFhREZGVnseIMGDQBITU11JKKIOKngar5Mua87vp7u7DiSxqvR68kvKDQ6log4KYcKSlxcHADh4eHFjjdq1AiA2NjYK8712muvsXDhwr9cPblo586dAAQHBzsSUUScWKMgP96++0Y8LGZWxSbzzqIt2shNRIrlUEE5efIkADVr1ix2PCAgALj2qx7Lly9n27ZtuLu707t372uaS0ScS5v6gbw+qDNmk4mfth3SRm4iUiw3R07Ozs4GLnw8U5yLx61W61UHio2N5cUXXwQurE8JCgr623NTUlLo1avX344vW7bsqnOISNmJahLCmNvaMWnhZv67eh/+lby4MzLM6FgiUkou996ckpJSok9HHLqCYrFYSnTe1V6y3bVrFw888AAZGRn06NGDp5566qrmERHn179tKI/0aA7AlF+3sXTPEYMTiYgzcegKiq+vLxkZGeTm5hY7npOTA4CPj4/DQX777TdeeOEFsrOz6dOnD++9994VC1FwcLCukoi4sAe7NeVMVg7Rmw7y+oINVPH2oEPo3181FRHXcLn35stdXfkzh66gBAYGApCWllbs+MW1JxfPK6np06czYsQIsrOzue+++/jggw/w8PBwaA4RcT0mk4kRN7emV7M65BcW8uJ3a9iXfNroWCLiBBwqKBfv3jl48GCx4/Hx8ZecdyWFhYW88MILTJ06FbPZzNixY3nllVcwm/WIIJGKwmI2M25AR9o3qIk1L58Rs1cSfzLD6FgiYjCHmkBUVBQAS5Ys+ctYeno6GzduxNPTk06dOpVovpdffpnvv/8eb29vpk+fzv333+9IHBEpJzzcLEy6qysRIdXJzMnjmVkxHDl15Q0fRaT8cqig9O7dm9q1axMTE8OcOXOKjufk5DB27FisViuDBw/G39+/aMxms5GQkEBCQgI2m63o+A8//MD8+fOxWCx8/PHH9OjRoxR+HBFxVT6e7rx3b3caBVXjTFYOT8+KISVDz+0RqahMdgdvudm8eTPDhw8nJyeHZs2aERISwvbt20lNTSUiIoJZs2bh6+tbdP6xY8eKFsQsW7aMkJAQCgoK6NWrFykpKdSsWZMOHTr87fe78cYb6d+//1+O/3lOkSvJzsun58QLj1dY/tIgvD0cWh8u19GZ8zn8+7/LOXzqHCH+lfh4WC9qVPY2OpaIlJKSvn87/Ld0+/btmTdvHh9++CGbNm0iPj6ekJAQBg8ezLBhwy4pJ38nNjaWlJQU4MLmbwsXLvzbc/38/IotKCJSPvlX8mLq0Cge+3IZx86c55lZMUx/sCfVfD2NjiZSYew+eor4kxnc3jYUk8lkSIar+jUyLCyMqVOnlujckJCQv2x937Rp0xJthy8iFVNgVR+m3d+Dx75cxqG0s4z8KoZpD/Sgkpfu7hMpa2vjjvPCnDXkFxYSGRpELb9KhuTQ7TIi4pRq+1di2v1RVPPx5EBKOqO/WUV2np6ALFKW1h9M4cXvLpSTmyLqElztyp+KlBUVFBFxWvUDqvJ/Q6Oo5OnOziOnVFJEytDG+BRemLMaW0EhPZqEMG5AR8M+3gEVFBFxcuHBfky5rzs+Hm5sS0rluW9WkaOSIlKqNh86wZg5a8grKKRb49qMH9QZN4uxFUEFRUScXkSdGvzf0Ch8PNzYmpTK6G9VUkRKy7bEkzz3zWry8gvoGlaLN5ygnIAKioi4iOZ1ajDlvj9KSmIqz327WiVF5BptT0pl1DeryM0voHOjYN4c3AV3t5I9GLisqaCIiMtoUbcG7//xcc+WxJOMmbOaHJtKisjV2HkkjVFfryLHVkBkaBATB3fFw0nKCaigiIiLaVk3gPfv7Y63uxubD53k+W9VUkQcteNwKs9+tZJsWz4dGtRk0pCueLo7TzkBFRQRcUEt6wUw5b4LJWXTxZKij3tESmTzoROM/Gol1rx82t4QyOQhN+Ll7ny7a6ugiIhLalkvgPfv61ZUUkZ+tZKsHNuVv1CkAlsXd5zRf3ys06lhMO/e0w0vJ330hwqKiLisVvUC+b+hUfh6urPjSBpPz17BWWuu0bFEnFLM/mM8f/FW4vDaTBrS1SmvnFykgiIiLq1F3RpMf6AHVb092Jd8hidnruDM+RyjY4k4ld93H+bluWvJLyykd7O6vDm4i1MtiC2OCoqIuLzwWv5Mf7An/r5exJ/M4PEvl5F6zmp0LBGnsGhHIq8t2ECB3c4tLevz2h0dnWKfkytx/oQiIiUQWrMaM/7Vi5pVfDhyOpPHv1jG8fTzRscSMdT8TQd544eNFNrt9G8bysv9I7GYXeOt3zVSioiUQJ3qlfn4X72o7efL8YwsHv9yGUlpZ42OJXLd2e12Pluxm3d/2QrA4Mgwnr+tHWazcc/WcZQKioiUK8HVfPl4WG/q16hC6rlsHv1iGXuPnTY6lsh1U1ho571ftvH5yr0ADI+KYMTNrQ198N/VUEERkXInoIo3Hw/rRdPa/pzLzuPJmctZf/C40bFEypwtv4BX569n/uaDmIDR/dryUFSEy5UTUEERkXKqmq8n0+7vQWRoEDm2Ap77ZjW/7Eg0OpZImbHm2nju29Us3XsEN7OZ8YM6c0eHRkbHumoqKCJSbvl4uvPO3TfSt3k9Cux2Jvywka/XHjA6lkipO2vN5elZMWxMOIG3uxvv3duN3hF1jY51TVRQRKRcc3ezMG5AR+7uFA7Ah7/vYOri7RQW2g1OJlI6jp3J5OHPlrI3+TRVvT2Y9kAPOoQGGR3rmqmgiEi5ZzabeLpva564qSUA366P5bUF68m1FRicTOTa7Dl6ioc/W8rRM5kEVfVhxr960SykutGxSoUKiohUGPd1acIrt0diMZv4fc8Rnp61gvQs7TorrmnFvqM8MXMFGdZcGgf78dnwm6gfUNXoWKVGBUVEKpR+rW5gyn3dqeTpzq6jp3j4s985fOqc0bFESsxut/PtugOMnbuWvPwCuobV4qNhvahe2dvoaKVKBUVEKpz2DYL4z/De1KrmS3J6Fg9/tpRtiSeNjiVyRQWFhbz/6zamLtmBHbijfSMmDemKt5M+kfhaqKCISIVUP6Aq/xl+ExEh1cnMyeOZ2StZpNuQxYllZucx+pvVRG+6sMfJ031aMapfG5fZut5R5fOnEhEpAf9KXkx7oAe9m9Ulv7CQN37YyEdLd1JQWGh0NJFLHD51juGf/c6G+BQ83Sy8ObgLd3du7JIbsJWUCoqIVGhe7m68fkcnHrixKQCz1+znuW9Wk5mdZ3AykQs2xKcw/D+/c+R0JjWr+PDJQ73p0bSO0bHKnAqKiFR4ZrOJx3q14PU7OuHpZmF9fAoP/ed3PWhQDHVxMeyor1dxPtdGizo1+OKRPoQH+xkd7bpQQRER+UOf5vX45KHeBFX14eiZTB76z++sOnDM6FhSAeXaCnjjh41MXbKDQrudf7RuwLQHeuBfycvoaNeNCoqIyJ+EB/vxxSN9aF0vAGtePs/PWcPnMXu086xcN8lnzvPI50v5ZWcSFpOJZ29pw4v/bI+Hm8XoaNeVCoqIyP/w8/Vi6v09GPTHg9Y+i9nDc9+u4qw11+BkUt6tjk1m2KeLiTuRTjUfT6YM7c6dkWHlejHs31FBEREphpvFzKh+bRnbvwMebhbWHUzhgRmL2XP0lNHRpBzKLyjko6U7GfPtajJzbESEVGfmo31p38D1n6lztVRQREQu47bWDfhseG/q+Ffm5Dkrj325jG/Xx2K36yMfKR1nzucwYnYMs9fsB2BwZBgfPdiTwKo+xgYzmAqKiMgVNAry48tH+tCzaR0KCu1MXbydF79bq1uR5ZptjE/h/hm/sTUpFW93NyYM6szIW9rgXsHWmxRHBUVEpAR8vdx5487OjOrXFjezmZUHjvHAJ4vZeSTN6Gjigmz5BUxbsoMRX63k9PkcbgiowheP3ETviLpGR3MaKigiIiVkMpkY1KERnz504Tk+KRlZ/PvL5Xy6fDf5Bdp9VkrmyKlzPPz5Ur5ZdwCAge0a8sUjfcrVk4hLgwqKiIiDmtT2Z+Zjfbm5RT0K7Xa+XLWXR79YytHTmUZHEydmt9v5efshHvxkCbEp6VTx9mDykK48d1s7vNzL38P+rpUKiojIVajk5cGrAzsx/o5OVPJ0Z1/yGR6YsZiftiVoAa38xenMbF6Ys4Y3f9xEti2ftvUDmf34zXRrHGJ0NKelyiYicg1ual6P5nVqMP77DWw/nMZbP21m1YFkxtzWjsAqFfsuDLlw1WTpniO8+8tWzmXn4WY2M7xHBPd1aVxun0JcWvRfR0TkGgVV82XaAz34d++WuFvMrI07zj3Tf9XVlAruzPkcxs5dy7j56zmXnUejoGp8+UgfHrixqcpJCegKiohIKbCYzQzt2oQuYbV488eN7Es+w1s/bWbpniO8+I/2BPtVMjqiXCd2u51le4/y3i9bybDmYjGbGNatGQ/c2BQ3i4pJSem/lIhIKWoQWJVPH+rNU31a4eFmYfOhk9z70W/M2xhHQaHu9Cnvks+cZ+RXK3kleh0Z1lxCA6vy+cN9eCgqQuXEQbqCIiJSyixmM/d0bsyN4bWZ+OMmdhxJ4/1ft/Hz9kSeu7UtEXVqGB1RSpktv4Cv1x3gy1X7yMsvwN1i5oEbm3J/1ybadO0qqaCIiJSROtUrM/3BnvywNYEZy3YSdyKdhz9fSv82DXi8d0uq+ngaHVFKwfakVN5ZtIXEtHMAtL0hkDG3tqNujSoGJ3NtKigiImXIbDYxsH1DopqE8NHSnSzakciP2w4Rs/8Yj/duyW2tb9CCSReVkn6eD3/fyfJ9RwHw8/Hk6b6t6duiXoV8+nBpU0EREbkO/Ct58fLtkfyjdQPeWbSFhNSzTFq4mehNB3mqTys6hFbcp9a6GmuujVlr9vPtugPkFRRiNpn4Z5sGPNarha6KlSIVFBGR66hlvQD++2hfojcd5IuVe4g/mcEzs2Po1DCYJ/u0okGgtjt3VgWFhfy6M4kZy3Zx+nwOAG3rB/LMza1pFORncLryRwVFROQ6c7OYGdIpnFta1ufLVXuZvyme9fEpbEw4wT9a38C/ukcQWFWbvDkLu91OzP5jfLp8N0mnLqwzqe1Xiaf6tKJb49r6OKeMqKCIiBikqo8nI25uwx3tG/HR0p3E7D/Gj9sO8cvOJG5vG8rQrk0JqOJtdMwKy263szHhBJ8s28WBlHQAKnt5cP+NTRgcGYaH7s4pUyooIiIGq1O9Mm/d1ZWdh9OYsXwXOw6nMW/TQX7cdogB7UIZ2qUJ1SurqFwvdrudTQkn+O+qfew4kgaAj4cbd3UM557O4VTy8jA4YcWggiIi4iRa1gvgowd7sjUxlf+s2M2uo6f4bkMcP2xJ4NbWN3B3p3BC/CsbHbPcKigsZOX+ZGat2UfsH1dMPCxm7ujQiKFdm+Dn62VwwopFBUVExImYTCbaNahJ2xsC2ZRwgs9i9rDn2GkWbI7nhy0JdG8Swr2dG9MspLrRUcuNHFs+S3Yf5uu1BzhyOhMAL3cL/duGck+nxloPZBAVFBERJ2QymYhsGEyH0CC2JaXy9doDrI9PYcW+o6zYd5RW9QK4o30jujeurZ1Kr1JKRhbfb47np20JnM3OAy6sMbkzshF3dgijmq9uGTaSCoqIiBMzmUy0vaEmbW+oScLJDL5Zd4Alu4+w43AaOw6n4e/rxT/aNKB/mwZ6IGEJFBQWsuXQSRZsiWdN7HEK/3jadHA1XwZ1aET/tqH4erobnFJABUVExGWE1qzGKwM68mivFvy4NYGftiZw6nwOM1fvY9bqfXRsGMwtLetzY3htvDz01/ufHTl1jl92JvHrzkRSz2UXHW/foCZ3dgijc1iwdvR1MvoTLCLiYgKr+PBwj+YM69aMNbHJLNgSz+ZDJ1kfn8L6+BR8PNzo3iSEvs3r0faGmhX2KbqnMrNZuf8Yi3cfZvfRU0XHK3t50Kd5PQZ1aEj9AG2M56xUUEREXJSbxUxU0zpENa3D0dOZ/LIzkSW7DnM8I4tfdybx684k/Hw86RJei27hIbQPrYmXe/n+a//k2Sxi9h9jxb6j7DpyCvsfx80mEx0bBtGv1Q10DauNp7vW7Ti78v0nVUSkgqhTvTKP9mzBIz2as/voaRbvTmLZniOkW3P5eXsiP29PxNPNQmRoEJ0aBdP2hpqE+Fdy+V1Q8/IL2HkkjY3xJ9iYcIL4kxmXjEeEVKdH0zr0aV6PGtpLxqWooIiIlCMmk4kWdWvQom4NRt7chu2HU1l9IJnVscmcOGtlVWwyq2KTAQiq6kPbG2rSpn4gzevUcInCkp2Xz77k0+w+eoqdR06x43AqObaConET0LJuAD2a1qF7k9rUrOprXFi5JiooIiLllJvFTPsGQbRvEMTIW9pw8EQGq2OT2ZJ4kj1HT3PirJVFOxJZtCMRuLA2o2ltf5rU9qdJLX9uCKhKLT9fwxaPZuXYSEg9S/zJdOJPnmV/8mkOnsigwG6/5LwalbzoEBp04bbsBkG6PbicUEEREakATCYTYcF+hAX78VBUBNl5+ew6ksbWxFS2JaVy8EQ6mTl5bEy48FHJRR4WM3WqV6Z+QBVq+1WiZlUfAqr4ULOKD4FVfKjs7X5VBcZut2PNzSfdmkNGVi4nzlpJycgiOf08x9PPc+zMeVIysor92sAq3rSoE0DzOjVoUz+Q0JpVnf7KjzhOBUVEpALy9nAjsmEwkQ2DAbDlF5CQepZ9yafZl3yGuBPpHD6VSd4fxxNSz/7tXD4eblTx9qCSlwfeHm5YzCYsJhMWswmz2YytoIBcWwG5+QXk5ReSnWcjIyuXvILCK+YMqOxNw5rVaFizGo2CqtG8Tg2Cquljm4pABUVERHB3s9C4lj+Na/kzsP2FYwWFhZzIsJJ06hxJaec4cTaL1LNWTp6zknrWSro1FwBrXj7WvHw4a3X4+3q6WfDz9aRGZW9C/CtRy++Pf6r50iCwKlV99HFNRaWCIiIixbKYzdT2r0Rt/0p0Cav1l3FbfgHnc22cz7FxLjuP8zl5ZNsKKCy0U1BYSKHdTn6hHXeLGU83Cx5uFjzdLXi5W/Dz8aSarxfe2lBO/ob+ZIiIyFVxd7Pg52bRU36lTFTM7QVFRETEqamgiIiIiNNRQRERERGno4IiIiIiTkcFRURERJyOCoqIiIg4HRWU/9GrVy969epldAy5BnoNXZ9eQ9em18/1OcNrqIIiIiIiTkcFRURERJyOCoqIiIg4HRUUERERcToqKCIiIuJ0VFBERETE6Zjsdrvd6BBXo3nz5hQUFBAcHFyq86akpACU+rxy/RT3GtrtkJKRdeF4NV9MJkOiSQnp/0PXptfP9ZXla5iSkoLFYmH37t2XPc9lC0q7du3Iy8sjICDA6CgiIiJSQmlpaXh4eLBly5bLnueyBUVERETKL61BEREREaejgiIiIiJORwVFREREnI4Kyh8SExMZPXo0PXr0oEWLFvTp04cpU6aQlZVldDS5CklJSbRq1Yo333zT6CjigB9//JGhQ4fSvn17IiIi6N69Oy+88AKHDh0yOpqUQGFhId9++y133HEHrVq1onXr1gwaNIivvvqK/Px8o+PJVXj66acJDw9nwYIF1/17q6AAu3btYuDAgSxcuJCAgACioqKwWq3MmDGDIUOGkJmZaXREccCpU6f497//TXZ2ttFRpITsdjujRo1izJgxbN++ndDQULp164bFYuH7779n4MCBrF+/3uiYcgUvvPACr732GvHx8bRu3Zr27duTlJTEhAkTGDZsGHl5eUZHFAfMmzePxYsXG/b93Qz7zk7CZrMxYsQIrFYrkyZNYsCAAQDk5OQwcuRIli9fznvvvcdrr71mbFApkf379/PMM89w+PBho6OIA3766Sd+/vlnAgMD+fzzzwkLCwOgoKCAqVOnMmPGDEaPHs3vv/+Oj4+PwWmlOD/++CM//vgjtWvX5quvvqJWrVoApKenM2zYMDZt2sSsWbMYPny4wUmlJBITE5k4caKhGSr8FZRFixaRnJxMly5disoJgJeXFxMnTsTHx4fo6GjOnTtnYEq5krNnz/LOO+8wePBgDh8+TEhIiNGRxAHR0dEAjBo1qqicAFgsFkaMGEGjRo04deoU69atMyqiXMH3338PwMiRI4vKCYCfnx+PPPIIAKtWrTIkmzgmLy+PUaNGYTabadq0qWE5KnxBWbFiBQB9+vT5y5ifnx+RkZHYbDbWrFlzvaOJA2bNmsVnn32Gv78/H3/8MbfffrvRkcQBVapUITQ0lLZt2/5lzGQyccMNNwCQmpp6vaNJCX366acsXLiQ3r17/2WssLAQAHd39+sdS67ClClT2Lt3L+PGjTN0N+AKX1Di4uIACA8PL3a8UaNGAMTGxl63TOK4oKAgnn/+eRYvXkzPnj2NjiMOmj59Or/88gt16tT5y1hBQQF79+4FtHW6M/Pw8CAsLAxvb+9LjickJDBt2jQABg4caEQ0ccC6dev48ssvufXWW+nfv7+hWSr8GpSTJ08CULNmzWLHL26lr9/cnNudd95pdAQpI9988w3Jycn4+fnRsWNHo+NICT3//PMkJCSwZ88evL29efHFF7n11luNjiWXcebMGcaMGUNQUJBTrLus8AXl4p0eXl5exY5fPG61Wq9bJhG5YP369bz99tvAhfUp//vbuTin8+fP88MPPxT9u8lk4siRI2RlZeHr62tcMLmsl156idOnTzNz5kyqVKlidBx9xGOxWEp0nh5ZJHJ9rVixgscee4y8vDzuueceXSVzIR4eHqxZs4Zt27Yxc+ZM6taty9dff80jjzyiv0ud1Ndff82KFSt46KGH6NChg9FxABWUojafm5tb7HhOTg6Abm0UuY5mz57NE088QU5ODkOHDmXcuHFGRxIHeHh4EBAQgK+vLx07duTLL78kICCALVu2sHLlSqPjyf84ePAgkydPplmzZjzzzDNGxylS4T/iCQwMJCMjg7S0tGIX4F1cexIYGHi9o4lUOPn5+YwfP57vvvsOk8nEqFGjim5RFdfl5+dH9+7diY6OZs+ePURFRRkdSf7k3XffJTc3Fy8vL1588cVLxi4uUJ87dy7r1q2jffv23HXXXdclV4UvKOHh4cTFxXHw4EFatGjxl/H4+Pii80Sk7OTk5PDEE0+wZs0avLy8mDx5MjfffLPRsaQE8vLyePfddzlx4gTvvPMOnp6efznHw8MDQFveO6GLayy3bt3K1q1biz1n+/btbN++HTc3NxWU6yUqKoqFCxeyZMkS7rjjjkvG0tPT2bhxI56ennTq1MmghCLlX0FBQVE58ff355NPPin2FwZxTh4eHvz222+cPHmSfv36/aVY5uXlFW2y17x5cyMiymXMnj37b8f+/e9/s2zZMt56663rfpt4hV+D0rt3b2rXrk1MTAxz5swpOp6Tk8PYsWOxWq0MHjwYf39/A1OKlG8ff/wxa9aswcfHh1mzZqmcuKB77rkHgIkTJ17yqAmr1crLL79MUlISYWFh+nhHSqzCX0G5eCl5+PDhvPrqq8ydO5eQkBC2b99OamoqERERjBw50uiYIuXW2bNn+fzzz4ELa70++eSTvz23f//+3HjjjdcrmjjgoYceYseOHaxYsYJbb72Vtm3b4unpye7duzlz5gx16tTho48+KvGdkyIVvqAAtG/fnnnz5vHhhx+yadMm4uPjCQkJYfDgwQwbNkz37YuUoU2bNhV9Bp6UlERSUtLfnhsREaGC4qTc3d356KOPmDt3LvPnz2fnzp0UFhZSt25d7r77boYNG0blypWNjikuxGTXTekiIiLiZCr8GhQRERFxPiooIiIi4nRUUERERMTpqKCIiIiI01FBEREREaejgiIiIiJORwVFREREnI4KioiIiDgdFRQRERFxOiooIiIi4nRUUERERMTpqKCIiIiI01FBEREREafz/wDrl77eRQ8fvwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fine, data_fit)\n",
    "\n",
    "plt.axvline(fine[np.argmin((data_fit-0.5)**2)])\n",
    "plt.axhline(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "b911138c-510c-4c12-ab3a-b715b3a04893",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-31T14:55:22.687068Z",
     "iopub.status.busy": "2024-03-31T14:55:22.686068Z",
     "iopub.status.idle": "2024-03-31T14:55:23.834785Z",
     "shell.execute_reply": "2024-03-31T14:55:23.833786Z",
     "shell.execute_reply.started": "2024-03-31T14:55:22.687068Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "############################### Fit #################################\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(pops[-1] - pops[-1].mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    \n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "plt.figure(figsize = (14,6))\n",
    "plt.subplot(1,3,1)\n",
    "\n",
    "for i in range(len(readout_freqs)):\n",
    "    guess = [(2*4.43)**-1,30,(-1)**(i//2+1)*0.5,(-1)**(i//2+1),0.9-i*np.pi]\n",
    "    # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "    plt.plot(fine,data_fit, color = colors[i])\n",
    "    if i==1: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "    plt.plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "\n",
    "    \n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = fine[np.argmin((data_fit-0.5)**2)]\n",
    "    \n",
    "plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "plt.title(plt_label, fontsize = \"small\")\n",
    "\n",
    "\n",
    "plt.xlim(x[0],x[-1])\n",
    "plt.xlabel(\"Rabi duration (ms)\")\n",
    "plt.ylabel(\"Population\")\n",
    "plt.axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "plt.legend(fontsize = \"small\", loc = \"center right\")\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "\n",
    "for i in range(len(readout_freqs)):\n",
    "    guess = [(2*4.43)**-1,30,(-1)**(i//2+1)*0.5,(-1)**(i//2+1),0.9-i*np.pi]\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "    plt.plot(fine,data_fit, color = colors[i])\n",
    "    plt.plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "\n",
    "plt.ylim(0.9,1)\n",
    "plt.xlim(T_pi*0.8,T_pi*1.2)\n",
    "plt.xlabel(\"Rabi duration (ms)\")\n",
    "plt.ylabel(\"Population\")\n",
    "plt.axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "\n",
    "for i in range(len(readout_freqs)):\n",
    "    guess = [(2*4.43)**-1,30,(-1)**(i//2+1)*0.5,(-1)**(i//2+1),0.9-i*np.pi]\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "    plt.plot(fine,data_fit, color = colors[i])\n",
    "    plt.plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "\n",
    "plt.ylim(0.4,0.6)\n",
    "plt.xlim(T_pio2*0.8,T_pio2*1.2)\n",
    "plt.xlabel(\"Rabi duration (ms)\")\n",
    "plt.ylabel(\"Population\")\n",
    "plt.axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "plt.grid()\n",
    "\n",
    "plt.tight_layout()\n",
    "save_fig_manustyle(path+\"\\\\\"+timestamp_list[0]+\"_raman_rabi_a_zooms.pdf\")\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41ed99da-a84f-493d-9f8d-a445e60dd355",
   "metadata": {},
   "source": [
    "### vs freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c66c8e51-e09e-4ee1-85c7-c3d6bc85ba14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1400 kHz high power, large range, b flipped\n",
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_rabi_a\\\\'\n",
    "timestamp_list = grab_timestamps_hdf(path, \"20231230111042\", \"20231230134146\")#\"20231230184346\")\n",
    "delta_freqs = np.linspace(-150, 150, 21)\n",
    "\n",
    "data_list = load_from_directory(path, timestamp_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "631d9773-1b96-46e3-b3c2-133b82f3fd41",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_guess = 0\n",
    "fit = 0\n",
    "Key_field='0'\n",
    "fig,ax=plt.subplots(1,1,figsize=(8,5))\n",
    "ax.set_ylabel(r\"P$^{b}_{|\\downarrow\\rangle}$\")\n",
    "ax.set_xlabel(\"Rabi duration (ms)\")\n",
    "ax.set_ylim(0,1)\n",
    "\n",
    "\n",
    "z_list = []\n",
    "pop_list = []\n",
    "for i in range(len(data_list)):\n",
    "    ###########################################################\n",
    "    data = data_list[i][0][Key_field]['click_array']\n",
    "    readout_freqs = data_list[i][0][Key_field]['readout_freqs']\n",
    "    nuclear_spin_freq_a_swept = data_list[i][0][Key_field]['nuclear_spin_freq_a_swept']\n",
    "    \n",
    "    raman_pulse_durations = (1e3+1e6*np.linspace(0,50,21))//4\n",
    "    raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "    sigmoid_length = 5\n",
    "    x = 4e-6*np.array(raman_pulse_durations)\n",
    "\n",
    "    ############################### Fit #################################\n",
    "    \n",
    "    if len(readout_freqs)==4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "        p_data = pops[-1]\n",
    "    else:\n",
    "        pops = []\n",
    "        p_data = (data[:,:,-1]>85).mean(0)\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    def rabi_fit(t,f,T,a,b,phi):\n",
    "        return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "    guess = [0.03,100,0.5,1,1*np.pi]\n",
    "\n",
    "    if fit:\n",
    "        try:est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "        except: \n",
    "            print(\"fit failed\")\n",
    "            est = guess\n",
    "            std = np.multiply(guess,0.1)\n",
    "            fine = x\n",
    "            data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "    ##############################################################    \n",
    "    \n",
    "    Tpi = (1-est[-1]/2/np.pi)/est[0]\n",
    "    T_pio2 = (0.75-est[-1]/2/np.pi)/est[0]\n",
    "    #print(f'pi pulse: {Tpi:.2f} | pi/2 pulse {T_pio2:.2f}')\n",
    "    \n",
    "    #plt.vlines(Tpi, 0, 1, color=colors[i])\n",
    "    #plt.vlines(T_pio2, 0, 1, color=colors[i])\n",
    "    ax.errorbar(x, p_data, label = f'{delta_freqs[i]:.2f}', fmt = \"o-\") \n",
    "    #ax.plot(fine,data_fit, color=colors[i])\n",
    "    if plot_guess: ax.plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "    ax.legend(fontsize = \"small\", loc = \"upper right\")\n",
    "    z_list.append(p_data)\n",
    "    pop_list.append(pops)\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "z_list = np.array(z_list)\n",
    "pop_list = np.array(pop_list)\n",
    "plot_2d_sweep(z_list, x=np.round(x, 2), y=np.round(delta_freqs[:len(data_list)],2), xlabel='Pulse duration (ms)', ylabel=r\"$\\delta$f (Hz)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0b6c6fc-41b3-4e2d-b449-8068ba5c7f9c",
   "metadata": {},
   "source": [
    "## Spin B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "e961dba8-fd0e-4cc4-b54f-b81056b62c8e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-31T15:15:47.771484Z",
     "iopub.status.busy": "2024-03-31T15:15:47.771484Z",
     "iopub.status.idle": "2024-03-31T15:15:49.958470Z",
     "shell.execute_reply": "2024-03-31T15:15:49.956469Z",
     "shell.execute_reply.started": "2024-03-31T15:15:47.771484Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape: (473, 21, 4)                                                      \n"
     ]
    }
   ],
   "source": [
    "path='Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_rabi_b'\n",
    "timestamp_list = [\"20240331071359\"]#grab_timestamps_hdf(path, \"20240318145645\",\"20240318145645\")\n",
    "data_list = load_from_directory(path, timestamp_list)\n",
    "\n",
    "Key_field = '0'\n",
    "data = data_list[0][0][Key_field]['click_array']\n",
    "raman_pulse_durations = (1e3+1e6*np.concatenate((sinhspace(0,3,11, nonlinearity = 3), sinhspace(3,6,11, nonlinearity = 2)[1:])))//4# data_list[0][0][Key_field]['raman_pulse_durations']\n",
    "\n",
    "print('Data shape:', data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "2f751419-0165-4cc8-bb4e-fe3edde24d70",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-31T15:15:49.961470Z",
     "iopub.status.busy": "2024-03-31T15:15:49.960470Z",
     "iopub.status.idle": "2024-03-31T15:15:51.894382Z",
     "shell.execute_reply": "2024-03-31T15:15:51.892387Z",
     "shell.execute_reply.started": "2024-03-31T15:15:49.961470Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(473, 21, 4)\n",
      "[0.06987100694182428, 100, 0.4, 0.9, 3.141592653589793]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\manipp102\\.conda\\envs\\qm37\\lib\\site-packages\\scipy\\optimize\\minpack.py:834: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  category=OptimizeWarning)\n",
      "C:\\Users\\manipp102\\.conda\\envs\\qm37\\lib\\site-packages\\scipy\\optimize\\minpack.py:834: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  category=OptimizeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x2500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_guess = 0\n",
    "###########################################################\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "\n",
    "############################### Fit #################################\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(pops[-1] - pops[-1].mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    \n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "\n",
    "#guess = [0.05,100,0.5,1,1*np.pi]\n",
    "guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "\n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = T_pi-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([x[0],x[-1]])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "            if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess)+0.05*i, color = colors[i],alpha = 0.5)\n",
    "\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==1: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "\n",
    "    \n",
    "# if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "try: \n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except: pass\n",
    "#ax[1].set_ylim(0,1)\n",
    "#ax[1].set_xlim(2.5,3.6)#x[0],x[-1])\n",
    "ax[1].set_xlim(x[0],x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()\n",
    "\n",
    "bins=np.arange(20,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(fft_x, fft_y)\n",
    "ax[3].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[3].set_xlabel(\"Frequency (Hz)\")\n",
    "ax[3].set_ylabel(\"FFT\")\n",
    "ax[3].set_title(f\"Max frequency = {rabi_freq:.2f} Hz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "save_fig_manustyle(path+\"\\\\\"+timestamp_list[0]+\"_raman_rabi_b_nuclear_spin.pdf\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "489dfacd-3e4d-4678-a3e7-69a6e8a453cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-31T15:15:51.897382Z",
     "iopub.status.busy": "2024-03-31T15:15:51.896383Z",
     "iopub.status.idle": "2024-03-31T15:15:52.066410Z",
     "shell.execute_reply": "2024-03-31T15:15:52.064409Z",
     "shell.execute_reply.started": "2024-03-31T15:15:51.897382Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1201773e408>"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fine, data_fit)\n",
    "\n",
    "plt.axvline(fine[np.argmin((data_fit-0.5)**2)])\n",
    "plt.axhline(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "2a6b3053-3eb1-4d05-a52f-af378a383f2f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-31T15:15:52.068410Z",
     "iopub.status.busy": "2024-03-31T15:15:52.067411Z",
     "iopub.status.idle": "2024-03-31T15:15:53.222621Z",
     "shell.execute_reply": "2024-03-31T15:15:53.220616Z",
     "shell.execute_reply.started": "2024-03-31T15:15:52.068410Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\manipp102\\.conda\\envs\\qm37\\lib\\site-packages\\scipy\\optimize\\minpack.py:834: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  category=OptimizeWarning)\n",
      "C:\\Users\\manipp102\\.conda\\envs\\qm37\\lib\\site-packages\\scipy\\optimize\\minpack.py:834: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  category=OptimizeWarning)\n",
      "C:\\Users\\manipp102\\.conda\\envs\\qm37\\lib\\site-packages\\scipy\\optimize\\minpack.py:834: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  category=OptimizeWarning)\n",
      "C:\\Users\\manipp102\\.conda\\envs\\qm37\\lib\\site-packages\\scipy\\optimize\\minpack.py:834: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  category=OptimizeWarning)\n",
      "C:\\Users\\manipp102\\.conda\\envs\\qm37\\lib\\site-packages\\scipy\\optimize\\minpack.py:834: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  category=OptimizeWarning)\n",
      "C:\\Users\\manipp102\\.conda\\envs\\qm37\\lib\\site-packages\\scipy\\optimize\\minpack.py:834: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  category=OptimizeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "############################### Fit #################################\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(pops[-1] - pops[-1].mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    \n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "plt.figure(figsize = (14,6))\n",
    "plt.subplot(1,3,1)\n",
    "\n",
    "for i in range(len(readout_freqs)):\n",
    "    guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "    plt.plot(fine,data_fit, color = colors[i])\n",
    "    if i==1: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "    plt.plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "\n",
    "    \n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = fine[np.argmin((data_fit-0.5)**2)]\n",
    "    \n",
    "plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "plt.title(plt_label, fontsize = \"small\")\n",
    "\n",
    "\n",
    "plt.xlim(x[0],x[-1])\n",
    "plt.xlabel(\"Rabi duration (ms)\")\n",
    "plt.ylabel(\"Population\")\n",
    "plt.axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "plt.legend(fontsize = \"small\", loc = \"center right\")\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "\n",
    "for i in range(len(readout_freqs)):\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "    plt.plot(fine,data_fit, color = colors[i])\n",
    "    plt.plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "\n",
    "plt.ylim(0.9,1)\n",
    "plt.xlim(T_pi*0.8,T_pi*1.2)\n",
    "plt.xlabel(\"Rabi duration (ms)\")\n",
    "plt.ylabel(\"Population\")\n",
    "plt.axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "\n",
    "for i in range(len(readout_freqs)):\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "    plt.plot(fine,data_fit, color = colors[i])\n",
    "    plt.plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "\n",
    "plt.ylim(0.4,0.6)\n",
    "plt.xlim(T_pio2*0.8,T_pio2*1.2)\n",
    "plt.xlabel(\"Rabi duration (ms)\")\n",
    "plt.ylabel(\"Population\")\n",
    "plt.axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "plt.grid()\n",
    "\n",
    "plt.tight_layout()\n",
    "save_fig_manustyle(path+\"\\\\\"+timestamp_list[0]+\"_raman_rabi_b_zooms.pdf\")\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55c7e7cb-32ad-44a6-953f-d414f43aca75",
   "metadata": {},
   "source": [
    "### vs freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96989677-587b-49b1-a1d0-5f96f7bca88c",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_rabi_a\\\\'\n",
    "[direc[:14] for direc in os.listdir(path) if 'hdf5' in direc][-21:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddf31e5f-1923-4752-b2b3-c9e5964caacf",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_rabi_b\\\\'\n",
    "timestamp_list = [\n",
    "    '20231219235244',\n",
    "    '20231220004306',\n",
    "    '20231220013328',\n",
    "    '20231220022350',\n",
    "    '20231220031413',\n",
    "    '20231220040435',\n",
    "    '20231220045457',\n",
    "    '20231220054520',\n",
    "    '20231220063542',\n",
    "    '20231220072604',\n",
    "    '20231220081626'\n",
    "]\n",
    "\n",
    "\n",
    "\n",
    "def load_from_directory(directory, timestamp_list):\n",
    "    data_list=[]\n",
    "    for timestamp in timestamp_list:\n",
    "        filenames=getfiles_hdf5(directory)\n",
    "        for filename in filenames:\n",
    "            if timestamp in filename: \n",
    "                PrintStatic(f'Loading {filename}')\n",
    "                data=load_h5_to_dic(directory+r'\\\\'+filename)\n",
    "                data_list.append(data)\n",
    "        PrintStatic(f'Loaded {len(data_list)} files')\n",
    "    return data_list\n",
    "\n",
    "data_list = load_from_directory(path, timestamp_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "faabe82d-900f-42d4-bff2-396df02c9a1e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e468c79f-71ec-418d-b890-e3e58b39bb9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_guess = 0\n",
    "\n",
    "Key_field='0'\n",
    "fig,ax=plt.subplots(1,1,figsize=(8,5))\n",
    "ax.set_ylabel(r\"P$^{b}_{|\\downarrow\\rangle}$\")\n",
    "ax.set_xlabel(\"Rabi duration (ms)\")\n",
    "ax.set_ylim(0,1)\n",
    "delta_freqs = np.linspace(-0.25, 0.25, 11)\n",
    "\n",
    "z_list = []\n",
    "pop_list = []\n",
    "for i in range(len(data_list)):\n",
    "    ###########################################################\n",
    "    data = data_list[i][0][Key_field]['click_array']\n",
    "    raman_pulse_durations = (1e3+1e6*np.linspace(0,10,data.shape[1]))//4\n",
    "    raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "    sigmoid_length = 5\n",
    "    x = 4e-6*np.array(raman_pulse_durations)\n",
    "\n",
    "    ############################### Fit #################################\n",
    "    \n",
    "    if len(readout_freqs)==4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "        p_data = pops[-1]\n",
    "    else:\n",
    "        pops = []\n",
    "        p_data = (data[:,:,-1]>85).mean(0)\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    def rabi_fit(t,f,T,a,b,phi):\n",
    "        return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "    guess = [0.05,100,0.5,1,1*np.pi]\n",
    "\n",
    "    try:est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        est = guess\n",
    "        std = np.multiply(guess,0.1)\n",
    "        fine = x\n",
    "        data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "    ##############################################################    \n",
    "    \n",
    "    Tpi = (1-est[-1]/2/np.pi)/est[0]\n",
    "    T_pio2 = (0.75-est[-1]/2/np.pi)/est[0]\n",
    "    #print(f'pi pulse: {Tpi:.2f} | pi/2 pulse {T_pio2:.2f}')\n",
    "    \n",
    "    #plt.vlines(Tpi, 0, 1, color=colors[i])\n",
    "    #plt.vlines(T_pio2, 0, 1, color=colors[i])\n",
    "    ax.errorbar(x, p_data, label = f'{delta_freqs[i]:.2f}', fmt = \"o-\") \n",
    "    #ax.plot(fine,data_fit, color=colors[i])\n",
    "    if plot_guess: ax.plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "    ax.legend(fontsize = \"small\", loc = \"upper right\")\n",
    "    z_list.append(p_data)\n",
    "    pop_list.append(pops)\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "z_list = np.array(z_list)\n",
    "pop_list = np.array(pop_list)\n",
    "plot_2d_sweep(z_list, x=x, y=np.round(delta_freqs,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "107a4cbb-5aa1-4a3e-8d16-a1c528eb64d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_list.mean(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fe88af3-0a3a-4660-a3c2-cb0a2184d6df",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_guess = 0\n",
    "\n",
    "Key_field='0'\n",
    "fig,ax=plt.subplots(1,1,figsize=(8,5))\n",
    "ax.set_ylabel(r\"P$^{b}_{|\\downarrow\\rangle}$\")\n",
    "ax.set_xlabel(\"Rabi duration (ms)\")\n",
    "ax.set_ylim(0,1)\n",
    "\n",
    "pops = pop_list[:5].mean(0)\n",
    "z = pops[-1]\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "guess = [0.05,100,0.5,1,1*np.pi]\n",
    "\n",
    "try:est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  z)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "##############################################################    \n",
    "\n",
    "Tpi = (1-est[-1]/2/np.pi)/est[0]\n",
    "T_pio2 = (0.75-est[-1]/2/np.pi)/est[0]\n",
    "print(f'pi pulse: {Tpi:.2f} | pi/2 pulse {T_pio2:.2f}')\n",
    "\n",
    "\n",
    "for p in pops:\n",
    "    ax.errorbar(x, p, fmt = \"o-\") \n",
    "ax.plot(fine,data_fit, label = f'$\\pi$ pulse = {Tpi:.2f} ms', color=colors[0])\n",
    "if plot_guess: ax.plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "ax.legend(fontsize = \"small\", loc = \"upper right\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "753e3d40-24a9-4875-be29-4bc1cc1e9a31",
   "metadata": {},
   "source": [
    "## 10 pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "b3aa98c5-e960-4660-8209-ba3309bf2a6c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-29T15:25:34.904499Z",
     "iopub.status.busy": "2024-03-29T15:25:34.904499Z",
     "iopub.status.idle": "2024-03-29T15:25:40.039650Z",
     "shell.execute_reply": "2024-03-29T15:25:40.037652Z",
     "shell.execute_reply.started": "2024-03-29T15:25:34.904499Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 1 files                                                                \r"
     ]
    }
   ],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_rabi_a\\\\'\n",
    "timestamp_list = [\"20240329130629\"]\n",
    "data_list = load_from_directory(path, timestamp_list)\n",
    "data=data_list[0][0][Key_field]['click_array']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "a883c328-3f83-4095-a0d4-c4f66cede422",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-29T15:25:40.041650Z",
     "iopub.status.busy": "2024-03-29T15:25:40.041650Z",
     "iopub.status.idle": "2024-03-29T15:25:41.057668Z",
     "shell.execute_reply": "2024-03-29T15:25:41.056665Z",
     "shell.execute_reply.started": "2024-03-29T15:25:40.041650Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.05, 100, 0.5, 1, 3.141592653589793]\n",
      "fit failed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\manipp102\\.conda\\envs\\qm37\\lib\\site-packages\\scipy\\optimize\\minpack.py:827: RuntimeWarning: invalid value encountered in multiply\n",
      "  pcov = pcov * s_sq\n",
      "C:\\Users\\manipp102\\.conda\\envs\\qm37\\lib\\site-packages\\scipy\\optimize\\minpack.py:834: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  category=OptimizeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "raman_pulse_durations = np.linspace(0.e6,0.2e6,11)//4 \n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(2,1,figsize=(10,10))\n",
    "sigmoid_length = 5\n",
    "\n",
    "# p_data = (data[:,:,-1]>threshold)\n",
    "# fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "# fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "# rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "############################### Fit #################################\n",
    "p_data = (data[:,:,-1]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    \n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "\n",
    "guess = [0.05,100,0.5,1,1*np.pi]\n",
    "#guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "\n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = T_pi-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([x[0],x[-1]])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            #guess = [1e-3*rabi_freq,30,(-1)**(i//2+1)*0.5,(-1)**(i//2+1),0.9-i*np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            \n",
    "            guess = [(2*4.43)**-1,30,(-1)**(i//2+1)*0.5,(-1)**(i//2+1),0.9-i*np.pi]\n",
    "            # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "            #ax[1].plot(fine,rabi_fit(fine,*guess)+0.05*i, color = colors[i],alpha = 0.5)\n",
    "\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==1: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "\n",
    "    \n",
    "# if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "try: \n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except: pass\n",
    "#ax[1].set_ylim(0,1)\n",
    "#ax[1].set_xlim(2.5,3.6)#x[0],x[-1])\n",
    "ax[1].set_xlim(x[0],x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "# ax[1].axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "4a2715c4-f6d2-4c46-860e-28bffd66e98f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-29T15:25:41.059668Z",
     "iopub.status.busy": "2024-03-29T15:25:41.059668Z",
     "iopub.status.idle": "2024-03-29T15:25:41.073666Z",
     "shell.execute_reply": "2024-03-29T15:25:41.072665Z",
     "shell.execute_reply.started": "2024-03-29T15:25:41.059668Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y1 = pops[3]\n",
    "x1 = x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "e3a640bb-13f8-448c-9074-e97512c9fae6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-29T15:34:09.096321Z",
     "iopub.status.busy": "2024-03-29T15:34:09.096321Z",
     "iopub.status.idle": "2024-03-29T15:34:14.188770Z",
     "shell.execute_reply": "2024-03-29T15:34:14.187768Z",
     "shell.execute_reply.started": "2024-03-29T15:34:09.096321Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 1 files                                                                \r"
     ]
    }
   ],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_rabi_a\\\\'\n",
    "timestamp_list = [\"20240329155832\"]\n",
    "data_list = load_from_directory(path, timestamp_list)\n",
    "data=data_list[0][0][Key_field]['click_array']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "74ca09b0-45d8-4fad-82e5-b30230eb9704",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-29T15:34:15.646962Z",
     "iopub.status.busy": "2024-03-29T15:34:15.645961Z",
     "iopub.status.idle": "2024-03-29T15:34:16.966113Z",
     "shell.execute_reply": "2024-03-29T15:34:16.965121Z",
     "shell.execute_reply.started": "2024-03-29T15:34:15.645961Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.05, 100, 0.5, 1, 3.141592653589793]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\manipp102\\.conda\\envs\\qm37\\lib\\site-packages\\scipy\\optimize\\minpack.py:834: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  category=OptimizeWarning)\n",
      "C:\\Users\\manipp102\\.conda\\envs\\qm37\\lib\\site-packages\\scipy\\optimize\\minpack.py:827: RuntimeWarning: invalid value encountered in multiply\n",
      "  pcov = pcov * s_sq\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fit failed\n",
      "fit failed\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "raman_pulse_durations = np.linspace(0.15e6,0.25e6,6)//4 \n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(2,1,figsize=(10,10))\n",
    "sigmoid_length = 5\n",
    "\n",
    "# p_data = (data[:,:,-1]>threshold)\n",
    "# fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "# fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "# rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "############################### Fit #################################\n",
    "p_data = (data[:,:,-1]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    \n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "\n",
    "guess = [0.05,100,0.5,1,1*np.pi]\n",
    "#guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "\n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = T_pi-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([x[0],x[-1]])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            #guess = [1e-3*rabi_freq,30,(-1)**(i//2+1)*0.5,(-1)**(i//2+1),0.9-i*np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            \n",
    "            guess = [(2*4.43)**-1,30,(-1)**(i//2+1)*0.5,(-1)**(i//2+1),0.9-i*np.pi]\n",
    "            # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "            #ax[1].plot(fine,rabi_fit(fine,*guess)+0.05*i, color = colors[i],alpha = 0.5)\n",
    "\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==1: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "\n",
    "    \n",
    "# if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "try: \n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except: pass\n",
    "#ax[1].set_ylim(0,1)\n",
    "#ax[1].set_xlim(2.5,3.6)#x[0],x[-1])\n",
    "ax[1].set_xlim(x[0],x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "# ax[1].axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "87d03c43-7c77-4f04-a0f9-930de2816a00",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-29T15:34:22.949803Z",
     "iopub.status.busy": "2024-03-29T15:34:22.949803Z",
     "iopub.status.idle": "2024-03-29T15:34:22.966804Z",
     "shell.execute_reply": "2024-03-29T15:34:22.964803Z",
     "shell.execute_reply.started": "2024-03-29T15:34:22.949803Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y2 = pops[3]\n",
    "x2 = x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "6ab916c4-4b82-4abe-b4bf-da77ad1e7ab4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-29T15:34:23.883746Z",
     "iopub.status.busy": "2024-03-29T15:34:23.882915Z",
     "iopub.status.idle": "2024-03-29T15:34:23.898747Z",
     "shell.execute_reply": "2024-03-29T15:34:23.897746Z",
     "shell.execute_reply.started": "2024-03-29T15:34:23.883746Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y = np.append(y1, y2)\n",
    "x = np.append(x1, x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "84b86831-faad-415a-aba3-447c0130c71e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-29T15:59:34.392972Z",
     "iopub.status.busy": "2024-03-29T15:59:34.392972Z",
     "iopub.status.idle": "2024-03-29T15:59:34.619989Z",
     "shell.execute_reply": "2024-03-29T15:59:34.618987Z",
     "shell.execute_reply.started": "2024-03-29T15:59:34.392972Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-2.06102489e+00  1.56109215e+05  1.51137723e-01  4.52668750e+00\n",
      " -6.51594042e+00]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(1, figsize=(10,5))\n",
    "\n",
    "    \n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = T_pi-T_half_period/2\n",
    "\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "\n",
    "guess = [0.3, 30, (-1)**(i//2+1)*0.5,(-1)**(i//2+1),0.9-i*np.pi]\n",
    "est,std,fine,data_fit = fit_function(guess, rabi_fit, x, y)\n",
    "\n",
    "# ax.plot(fine,data_fit, color = colors[3])\n",
    "\n",
    "q1, q2, q3 = np.polyfit(x, y, 2)\n",
    "ax.plot(fine, q1*fine**2 + q2*fine + q3, label = labels[3], color = colors[3])\n",
    "\n",
    "ax.plot(x,y,\"o\", label = labels[3], color = colors[3])\n",
    "\n",
    "\n",
    "    \n",
    "# if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "# try: \n",
    "#     plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "#     ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "# except: pass\n",
    "ax.set_ylim(0,1)\n",
    "#ax[1].set_xlim(2.5,3.6)#x[0],x[-1])\n",
    "ax.set_xlim(x[0],x[-1])\n",
    "ax.set_xlabel(\"Rabi duration (ms)\")\n",
    "ax.set_ylabel(\"Population\")\n",
    "# ax[1].axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "ax.legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax.grid()\n",
    "\n",
    "print(est)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58d98c8b-a3bd-46c2-9414-7af3f9791f6f",
   "metadata": {},
   "source": [
    "# Raman Ramsey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4898b4ff-e463-4155-ba38-c8083cd321ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_ramsey\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = [\n",
    "    '20231212225528',\n",
    "]\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    filenames=getfiles_hdf5(path)\n",
    "    for filename in filenames:\n",
    "        if timestamp in filename: \n",
    "            print('Loading '+path+filename)\n",
    "            data=load_h5_to_dic(path+filename)\n",
    "            data_list.append(data)\n",
    "    \n",
    "def lorentz(delta,delta0,kappa,a,b):\n",
    "    return a/(1+(delta-delta0)**2/(kappa/2)**2)+b\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61ff21bd-46ec-453f-974c-df94dcf25be7",
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath_list = ['Z:\\\\SMPD3-8\\\\SpinRun3\\\\raman_ramsey\\\\20231207141758_\\\\20231207141758_raman_ramsey']\n",
    "\n",
    "data_list = []\n",
    "for filepath in filepath_list:\n",
    "    filename=getfiles_hdf5(filepath)\n",
    "    data=load_h5_to_dic(filepath+r'\\\\'+filename[0])\n",
    "    data_list.append(data)\n",
    "    print(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bb48a5d-a8db-44d6-ac37-8108859ae9d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "shift_ac =  0.1385e-6\n",
    "\n",
    "Key_field = '0'\n",
    "ramsey_times=data_list[0][0][Key_field]['ramsey_times']\n",
    "data=data_list[0][0][Key_field]['click_array']\n",
    "ramsey_detuning=data_list[0][0][Key_field]['ramsey_detuning']\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(4,1,figsize=(5,15),tight_layout=True)\n",
    "sigmoid_length = 5\n",
    "x = 1e-6*np.array(ramsey_times)\n",
    "############################### Fit #################################\n",
    "\n",
    "plot_guess=0\n",
    "threshold=85\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    \n",
    "def ramsey_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "i=0\n",
    "ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel(r'$2\\tau$ (ms)')\n",
    "ax[0].legend()\n",
    "\n",
    "guess = [20*(ramsey_detuning-shift_ac)*1e3, 1e3, -0.5,-1,0*np.pi]\n",
    "est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, p_data.mean(0))\n",
    "T_half_period = 0.5/est[0]\n",
    "T_min = 0.5/(est[0]+std[0])\n",
    "T_max = 0.5/(est[0]-std[0])\n",
    "T_err = T_half_period-T_min\n",
    "\n",
    "\n",
    "# T_pi = fine[np.argmin(data_fit[:int(len(fine)*T_half_period*2/max(fine))])]\n",
    "# T_pio2 = T_pi-T_half_period/2\n",
    "T_pi_list=(-est[3]+np.arange(-5,5)*np.pi)/(2*np.pi*est[0])\n",
    "T_pi_list_p=T_pi_list[T_pi_list>0]\n",
    "T_pi = T_pi_list_p[np.argmin(T_pi_list_p)]\n",
    "T_pio2 = T_pi_list_p[np.argmin(T_pi_list_p)]-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "\n",
    "bins=np.arange(np.min(np.concatenate(data)),np.max(np.concatenate(data)),5)\n",
    "h2 = ax[2].hist(np.concatenate(data)  , bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].vlines(threshold, 0, np.max(h2[0]))\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "ax[1].plot(fine,data_fit)\n",
    "if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel(r'$\\tau$ (ms)')\n",
    "ax[1].legend()\n",
    "plt_label = f'f = {est[0]:.5f}$\\pm${std[0]:.5f} kHz, $\\delta $f = {(est[0] - (ramsey_detuning-shift_ac)*1e6):.4f} kHz \\n T$_2^*$ = {est[1]:.0f}$\\pm${std[1]:.0f} ms'\n",
    "T2_star = est[1]\n",
    "ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "ax[1].set_ylim(0,1)\n",
    "\n",
    "\n",
    "np.savetxt(filepath_list[0]+\"Ramsey_plotdata.txt\", np.transpose([x,p_data.mean(0)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c4a389f-e19e-41f5-a92f-590f13f12347",
   "metadata": {},
   "outputs": [],
   "source": [
    "chunk = 21\n",
    "plot_guess = 0\n",
    "plt.figure(figsize=[5,3*data.shape[0]//chunk*2])\n",
    "Ramsey_freqs = []\n",
    "Ramsey_freq_errors = []\n",
    "T2stars = []\n",
    "T2star_errors = []\n",
    "\n",
    "for i in range(data.shape[0]//chunk):\n",
    "\n",
    "    y = (data[i*chunk:(i+1)*chunk,:,0]>85).mean(0)\n",
    "    plt.subplot(data.shape[0]//chunk*2,1,i*2+1)\n",
    "    est,std,fine,data_fit = fit_function(guess, ramsey_fit, x,y)\n",
    "    T2stars.append(est[1])\n",
    "    Ramsey_freqs.append(est[0])\n",
    "    Ramsey_freq_errors.append(std[0])\n",
    "    T2star_errors.append(std[1])\n",
    "    \n",
    "    plt.plot(x, y,\"o-\")\n",
    "    \n",
    "    if plot_guess: plt.plot(fine,ramsey_fit(fine, *guess))\n",
    "    plt.plot(fine,data_fit)\n",
    "    plt.ylabel(r\"$P_{~|\\downarrow\\downarrow\\rangle}$\")\n",
    "    plt.xlabel('Ramsey time (ms)')\n",
    "    \n",
    "    ### Plot 4 - FFT ###\n",
    "    fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "    fft_y = np.abs(np.fft.rfft(y- y.mean()))\n",
    "    rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    \n",
    "    plt.subplot(data.shape[0]//chunk*2,1,i*2+2)\n",
    "    plt.plot(fft_x, fft_y)\n",
    "    plt.vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "    plt.xlabel(\"Frequency (kHz)\")\n",
    "    plt.ylabel(\"FFT\")\n",
    "    plt.title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9aa8456-9cb7-4d37-9a52-64a718eea306",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2stars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9db56942-a5bd-4eaa-9746-c041b9af0d15",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_star"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "621c6770-2b46-4b80-9d30-f3591f1d1a21",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.errorbar(2*np.linspace(0,len(Ramsey_freqs)-1,len(Ramsey_freqs)),np.multiply(1e3,Ramsey_freqs),yerr = np.multiply(1e3,Ramsey_freq_errors), fmt = \"o\", capsize = 10)\n",
    "plt.ylabel(\"Ramsey frequency (Hz)\")\n",
    "plt.xlabel(\"Time (hr)\")\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.errorbar(2*np.linspace(0,len(T2stars)-1,len(T2stars)),T2stars, yerr =T2star_errors, fmt = \"o\", capsize = 10)\n",
    "plt.hlines(T2_star, 0, 2*len(Ramsey_freqs), color='k', linestyle='dashed')\n",
    "plt.ylabel(r\"$T_2$ (ms)\")\n",
    "plt.xlabel(\"Time (hr)\")\n",
    "plt.ylim([0, 1200])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07a96b19-814f-4c97-a1aa-c54ca1aedcb4",
   "metadata": {},
   "source": [
    "# SEDOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed48d849-d22b-4863-bfdc-87b57afa654f",
   "metadata": {},
   "outputs": [],
   "source": [
    "path='Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\SEDOR'\n",
    "timestamp_list = grab_timestamps_hdf(path, \"20240214195708\",\"20240215015955\")\n",
    "data_list = load_from_directory(path, timestamp_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf0f4cc0-330e-4926-88bf-05939a01cc78",
   "metadata": {},
   "outputs": [],
   "source": [
    "Key_field = '0'\n",
    "ramsey_times=data_list[0][0][Key_field]['ramsey_times']\n",
    "data=data_list[0][0][Key_field]['click_array']\n",
    "ramsey_detuning=data_list[0][0][Key_field]['ramsey_detuning']\n",
    "\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(4,1,figsize=(8,15))\n",
    "sigmoid_length = 5\n",
    "x = 2*np.array(ramsey_times)\n",
    "############################### Fit #################################\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data=p2\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    p_data = (data[:,:,-1]>80)\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data - p_data.mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel(r'$2\\tau$ (ms)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "##############################################################    \n",
    "\n",
    "fit_params_all = []\n",
    "    \n",
    "def ramsey_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "guess = [rabi_freq*1e-3, 3e3, 0.5, 1, 0.5*np.pi]\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)): \n",
    "        try: \n",
    "            guess = [rabi_freq*1e-3, 3e3, 0.5, 1, (1+(-1)**i)*0.5*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, pops[i])\n",
    "            fit_params_all.append([est,std])\n",
    "        except:\n",
    "            fit_params_all.append([guess,guess])\n",
    "        ax[1].plot(x,pops[i], 'o', label = labels[i])\n",
    "        if i==0 or i ==2: ax[1].plot(fine,data_fit, color = colors[i])\n",
    "\n",
    "else:\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "    ax[1].plot(fine,data_fit)\n",
    "\n",
    "est = fit_params_all[0][0]\n",
    "std = fit_params_all[0][1]\n",
    "    \n",
    "plot_guess = 0\n",
    "if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel(r'$2\\tau$ (ms)')\n",
    "ax[1].legend()\n",
    "plt_label = f'f = {est[0]*1e3:.3f}$\\pm${std[0]*1e3:.3f} Hz, $\\delta $f = {(est[0] - ramsey_detuning*0.5e6)*1e3:.3f} Hz \\n T$_2^*$ = {est[1]*1e-3:.1f}$\\pm${std[1]*1e-3:.1f} s'\n",
    "ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "ax[1].set_ylim(0,1)\n",
    "\n",
    "##############################################################\n",
    "bins=np.arange(np.min(np.concatenate(data)),np.max(np.concatenate(data)),5)\n",
    "for index in range(4):h2 = ax[2].hist(data[:,:,index].mean(axis = 0),bins = bins,  label = labels[index], alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "##############################################################\n",
    "\n",
    "\n",
    "ax[3].plot(fft_x, fft_y)\n",
    "ax[3].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[3].set_xlabel(\"Frequency (kHz)\")\n",
    "ax[3].set_ylabel(\"FFT\")\n",
    "ax[3].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "pops_bg = pops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55cc3060-53f5-4a12-969e-2c5fece4c5c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "Key_field = '0'\n",
    "ramsey_times=data_list[1][0][Key_field]['ramsey_times']\n",
    "data=data_list[1][0][Key_field]['click_array']\n",
    "ramsey_detuning=data_list[1][0][Key_field]['ramsey_detuning']\n",
    "\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(4,1,figsize=(8,15))\n",
    "sigmoid_length = 5\n",
    "x = 2*np.array(ramsey_times)\n",
    "############################### Fit #################################\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data=p2\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    p_data = (data[:,:,-1]>80)\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data - p_data.mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel(r'$2\\tau$ (ms)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "##############################################################    \n",
    "\n",
    "fit_params_all = []\n",
    "    \n",
    "def ramsey_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "guess = [rabi_freq*1e-3, 3e3, 0.5, 1, 0.5*np.pi]\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)): \n",
    "        try: \n",
    "            guess = [rabi_freq*1e-3, 3e3, 0.5, 1, ((-1)**i)*0.5*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, pops[i])\n",
    "            fit_params_all.append(est)\n",
    "        except:\n",
    "            fit_params_all.append(guess)\n",
    "        ax[1].plot(x,pops[i], 'o', label = labels[i])\n",
    "        if i==1 or i ==3: ax[1].plot(fine,data_fit, color = colors[i])\n",
    "else:\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "    ax[1].plot(fine,data_fit)\n",
    "\n",
    "plot_guess = 0\n",
    "if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel(r'$2\\tau$ (ms)')\n",
    "ax[1].legend()\n",
    "plt_label = f'f = {est[0]*1e3:.3f}$\\pm${std[0]*1e3:.3f} Hz, $\\delta $f = {(est[0] - ramsey_detuning*0.5e6)*1e3:.3f} Hz \\n T$_2^*$ = {est[1]*1e-3:.1f}$\\pm${std[1]*1e-3:.1f} s'\n",
    "ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "ax[1].set_ylim(0,1)\n",
    "\n",
    "##############################################################\n",
    "bins=np.arange(np.min(np.concatenate(data)),np.max(np.concatenate(data)),5)\n",
    "for index in range(4):h2 = ax[2].hist(data[:,:,index].mean(axis = 0),bins = bins,  label = labels[index], alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "##############################################################\n",
    "\n",
    "\n",
    "ax[3].plot(fft_x, fft_y)\n",
    "ax[3].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[3].set_xlabel(\"Frequency (kHz)\")\n",
    "ax[3].set_ylabel(\"FFT\")\n",
    "ax[3].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "pops_ro = pops\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c47211e6-7c3c-42fd-96f6-af7a7b725cf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = (12,10))\n",
    "\n",
    "##############################################################    \n",
    "\n",
    "fit_params_all = []\n",
    "    \n",
    "def ramsey_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "guess = [rabi_freq*1e-3, 3e3, 0.5, 1, 0.5*np.pi]\n",
    "\n",
    "##############################################################\n",
    "plt.subplot(2,1,1)\n",
    "pops = pops_bg\n",
    "\n",
    "for i in range(len(readout_freqs)): \n",
    "    try: \n",
    "        guess = [rabi_freq*1e-3, 3e3, 0.5, 1, (1+(-1)**i)*0.5*np.pi]\n",
    "        est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, pops[i])\n",
    "        fit_params_all.append([est,std])\n",
    "    except:\n",
    "        fit_params_all.append([guess,guess])\n",
    "    plt.plot(x,pops[i], 'o', label = labels[i])\n",
    "    if i==0 or i ==2: plt.plot(fine,data_fit, color = colors[i])\n",
    "\n",
    "\n",
    "est = fit_params_all[0][0]\n",
    "std = fit_params_all[0][1]\n",
    "    \n",
    "plot_guess = 0\n",
    "if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "\n",
    "plt.ylabel(\"P\")\n",
    "plt.xlabel(r'$2\\tau$ (ms)')\n",
    "plt.legend(loc = \"upper right\")\n",
    "plt_label = f'f = {est[0]*1e3:.3f}$\\pm${std[0]*1e3:.3f} Hz, $\\delta $f = {(est[0] - ramsey_detuning*0.5e6)*1e3:.3f} Hz T$_2^*$ = {est[1]*1e-3:.1f}$\\pm${std[1]*1e-3:.1f} s'\n",
    "plt.title(plt_label, fontsize = \"small\")\n",
    "plt.xlim(0,4000)\n",
    "plt.ylim(0,1)\n",
    "##############################################################    \n",
    "plt.subplot(2,1,2)\n",
    "pops = pops_ro\n",
    "\n",
    "for i in range(len(readout_freqs)): \n",
    "    try: \n",
    "        guess = [rabi_freq*1e-3, 3e3, 0.5, 1, ((-1)**i)*0.5*np.pi]\n",
    "        est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, pops[i])\n",
    "        fit_params_all.append([est,std])\n",
    "    except:\n",
    "        fit_params_all.append([guess,guess])\n",
    "    plt.plot(x,pops[i], 'o', label = labels[i])\n",
    "    if i==1 or i ==3: plt.plot(fine,data_fit, color = colors[i])\n",
    "\n",
    "\n",
    "est = fit_params_all[-1][0]\n",
    "std = fit_params_all[-1][1]\n",
    "    \n",
    "plot_guess = 0\n",
    "if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "\n",
    "plt.ylabel(\"P\")\n",
    "plt.xlabel(r'$2\\tau$ (ms)')\n",
    "plt.legend(loc = \"upper right\")\n",
    "plt_label = f'f = {est[0]*1e3:.3f}$\\pm${std[0]*1e3:.3f} Hz, $\\delta $f = {(est[0] - ramsey_detuning*0.5e6)*1e3:.3f} Hz T$_2^*$ = {est[1]*1e-3:.1f}$\\pm${std[1]*1e-3:.1f} s'\n",
    "plt.title(plt_label, fontsize = \"small\")\n",
    "plt.xlim(0,4000)\n",
    "plt.ylim(0,1)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b79fd0d-ec62-47d1-b995-08705eae9bc1",
   "metadata": {},
   "source": [
    "# Raman Hahn echo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42cfd1c6-f54a-4238-bfef-dbf5f1544f77",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_list=[]\n",
    "path = \"Z:\\\\SMPD3-8\\\\SpinRun3\\\\raman_echo_alternating\\\\20231207175742_\\\\20231207175742_raman_echo_alternating\\\\\"\n",
    "timestamp = \"20231207175742\"\n",
    "\n",
    "filenames = getfiles(path,'.hdf5')\n",
    "data=load_h5_to_dic(path+filenames[0])\n",
    "data_list.append(data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a053e24-d889-412c-8bbd-56bb4d736a09",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "shift_ac =  0.1385e-6\n",
    "\n",
    "Key_field = '0'\n",
    "ramsey_times=data_list[0][0][Key_field]['ramsey_times']\n",
    "data=data_list[0][0][Key_field]['click_array']\n",
    "ramsey_detuning=data_list[0][0][Key_field]['ramsey_detuning']\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(4,1,figsize=(5,15),tight_layout=True)\n",
    "sigmoid_length = 5\n",
    "x = np.array(2*ramsey_times)\n",
    "############################### Fit #################################\n",
    "\n",
    "plot_guess=0\n",
    "threshold=85\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    \n",
    "def ramsey_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "i=0\n",
    "ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel(r'$2\\tau$ (ms)')\n",
    "ax[0].legend()\n",
    "\n",
    "guess = [4e-4, 1e3, -0.5,-1,0*np.pi]\n",
    "est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, p_data.mean(0))\n",
    "T_half_period = 0.5/est[0]\n",
    "T_min = 0.5/(est[0]+std[0])\n",
    "T_max = 0.5/(est[0]-std[0])\n",
    "T_err = T_half_period-T_min\n",
    "\n",
    "\n",
    "# T_pi = fine[np.argmin(data_fit[:int(len(fine)*T_half_period*2/max(fine))])]\n",
    "# T_pio2 = T_pi-T_half_period/2\n",
    "T_pi_list=(-est[3]+np.arange(-5,5)*np.pi)/(2*np.pi*est[0])\n",
    "T_pi_list_p=T_pi_list[T_pi_list>0]\n",
    "T_pi = T_pi_list_p[np.argmin(T_pi_list_p)]\n",
    "T_pio2 = T_pi_list_p[np.argmin(T_pi_list_p)]-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "\n",
    "bins=np.arange(np.min(np.concatenate(data)),np.max(np.concatenate(data)),5)\n",
    "h2 = ax[2].hist(np.concatenate(data)  , bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].vlines(threshold, 0, np.max(h2[0]))\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "ax[1].plot(fine,data_fit)\n",
    "if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel(r'$2\\tau$ (ms)')\n",
    "ax[1].legend()\n",
    "plt_label = f'f = {est[0]:.5f}$\\pm${std[0]:.5f} kHz, $\\delta $f = {(est[0] - (ramsey_detuning-shift_ac)*1e6):.4f} kHz \\n T$_2^*$ = {est[1]:.0f}$\\pm${std[1]:.0f} ms'\n",
    "T2_star = est[1]\n",
    "ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "ax[1].set_ylim(0,1)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(path+timestamp+\"_T2_analysis.pdf\")\n",
    "np.savetxt(path+timestamp+\"_T2_analysis.txt\", np.transpose([x, p_data.mean(0)]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3f66b56-1b98-4d52-b73c-1eac870e588f",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# Raman spectroscopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "689d7c1b-d730-4a1a-9d31-38fc0cca4763",
   "metadata": {},
   "outputs": [],
   "source": [
    "path='Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_spectroscopy'\n",
    "timestamp_list = grab_timestamps_hdf(path, \"20240222073419\",\"20240222111949\")\n",
    "data_list = load_from_directory(path, timestamp_list)\n",
    "def lorentz(delta,delta0,kappa,a,b):\n",
    "    return a/(1+(delta-delta0)**2/(kappa/2)**2)+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "826b9474-45db-4d44-ac6a-468f5d2ec671",
   "metadata": {},
   "outputs": [],
   "source": [
    "lorentz_func = lambda x,*y: y[0]/((x-y[2])**2+y[1]**2)+y[3]\n",
    "Key_field='0'\n",
    "\n",
    "pops_all = []\n",
    "\n",
    "for ii in range(len(data_list)):\n",
    "    data=data_list[ii][0][Key_field]['click_array']\n",
    "    nuclear_spin_frequencies=data_list[ii][0][Key_field]['nuclear_spin_frequencies']\n",
    "    freq_range=data_list[ii][0][Key_field]['readout_freqs']\n",
    "\n",
    "    detuned_electron_amplitude=data_list[ii][0][Key_field]['detuned_electron_amplitude_a']\n",
    "    detuned_sideband_amplitude=0.15\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "    fig,ax=plt.subplots(2,1,figsize=(12,9))\n",
    "    sigmoid_length = 5\n",
    "    x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "    ############################### extract populations #################################\n",
    "\n",
    "    p_data=(data>120)\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    pops_all.append(pops)\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    for i in range(len(freq_range)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[0].legend()\n",
    "    ax[0].set_title(f'Allowed power : {detuned_electron_amplitude:.2f} , Sideband power : {detuned_sideband_amplitude:.2f}')\n",
    "\n",
    "    bins=np.arange(50,np.max(np.concatenate(data)),5)\n",
    "    ax[1].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "    ax[1].legend()\n",
    "    ax[1].set_ylabel(\"instances\")\n",
    "    ax[1].set_xlabel('counts')\n",
    "\n",
    "    counts = np.array([x, *list(data.mean(0).T)]).T\n",
    "    pop_ = np.array([x, *pops]).T\n",
    "    \n",
    "    np.savetxt(f'data/counts_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.txt',counts)        \n",
    "    np.savetxt(f'data/pops_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.txt',pop_)  \n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'data/spectrocsopy_plot_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.pdf')\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c034e544-0249-4cea-a82c-c4495d8bbf77",
   "metadata": {},
   "outputs": [],
   "source": [
    "sigmoid_length = 5\n",
    "nuclear_spin_frequencies=data_list[0][0][Key_field]['nuclear_spin_frequencies']\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "plot_guess = 0\n",
    "############################### extract populations #################################\n",
    "p_data = []\n",
    "states_to_fit = [3,2]\n",
    "xs =  []\n",
    "fit= []\n",
    "for i in range(2):\n",
    "    data=data_list[i][0][Key_field]['click_array']\n",
    "    nuclear_spin_frequencies=data_list[i][0][Key_field]['nuclear_spin_frequencies']\n",
    "    xs.append(1e-3*np.array(nuclear_spin_frequencies))\n",
    "    if len(readout_freqs) == 4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        p_data.append(pops[states_to_fit[i]])\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    ############################### Fit ###############################\n",
    "\n",
    "    guess = [xs[-1][np.argmin(p_data[-1])],(max(xs[-1])-min(xs[-1]))/8, min(p_data[-1])-max(p_data[-1]), max(p_data[-1])]\n",
    "    try: \n",
    "        fit.append(fit_function(guess, lorentz, xs[-1], p_data[-1]))\n",
    "        fit_success = 1\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        fit_success = 0\n",
    "        fine = np.linspace(xs[-1][0],xs[-1][-1],len(xs[-1])*100)\n",
    "\n",
    "pulse_bandwidth = 1/(int(3.73e6//4)*4*1e-9)\n",
    "\n",
    "amplitude = 1\n",
    "cos_ramp_time = int(0.4e6/4)\n",
    "top_wait_time = int(3.73e6//4) \n",
    "center_freq = int(808.79e3)\n",
    "around_pulse = 10e6\n",
    "vals = np.concatenate(([0]*int(around_pulse/4),chirp_cos_raise(cos_ramp_time,amplitude,0)[0], [amplitude]*top_wait_time, chirp_cos_raise(cos_ramp_time,-amplitude,0)[0]+amplitude,[0]*int(around_pulse/4)))\n",
    "times = np.linspace(0,len(vals)*4*1e-6,len(vals))\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(times),d=times[1]-times[0])\n",
    "fft_y = np.abs(np.fft.rfft(vals))\n",
    "full_pulse = np.concatenate((np.flip(fft_y),fft_y))\n",
    "full_freq = np.concatenate((-np.flip(fft_x),fft_x))\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.ylim(0,1)\n",
    "plt.xlim(xs[0][0],xs[0][-1])\n",
    "plt.xlabel('Nuclear drive frequency (kHz)')\n",
    "plt.ylabel(\"Population\")\n",
    "# plt.plot(xs[0], p_data[0], \"o\", label = labels[states_to_fit[0]],color= colors[states_to_fit[0]]) \n",
    "# plt.plot(xs[1], p_data[1], \"o\", label = labels[states_to_fit[1]],color= colors[states_to_fit[1]])\n",
    "plt.plot((full_freq+center_freq)/1e3,full_pulse/(5*max(full_pulse)),\"k--\")\n",
    "for i in range(2):\n",
    "    plt.plot(xs[i], p_data[i], \"o\", label = labels[states_to_fit[i]],color= colors[states_to_fit[i]]) \n",
    "    plt.plot(fit[i][2],fit[i][3], label = f\"centre freq = {np.round(fit[i][0][0],3)} kHz $\\sigma$ = {np.round(fit[i][0][1],2)} kHz\",color = colors[states_to_fit[i]])\n",
    "if plot_guess: plt.plot(fine,lorentz(fine,*guess))\n",
    "\n",
    "plt.legend(fontsize = \"small\", loc = \"upper right\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75cc43d0-f1d1-47bd-b84e-4b3c5b8b1dab",
   "metadata": {},
   "outputs": [],
   "source": [
    "path='Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_spectroscopy'\n",
    "timestamp_list = grab_timestamps_hdf(path, \"20240222140109\",\"20240222141310\")\n",
    "data_list = load_from_directory(path, timestamp_list)\n",
    "def lorentz(delta,delta0,kappa,a,b):\n",
    "    return a/(1+(delta-delta0)**2/(kappa/2)**2)+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93023d08-695f-4b75-9c08-bf8a0669f776",
   "metadata": {},
   "outputs": [],
   "source": [
    "lorentz_func = lambda x,*y: y[0]/((x-y[2])**2+y[1]**2)+y[3]\n",
    "Key_field='0'\n",
    "\n",
    "pops_all = []\n",
    "\n",
    "for ii in range(len(data_list)):\n",
    "    data=data_list[ii][0][Key_field]['click_array']\n",
    "    nuclear_spin_frequencies=data_list[ii][0][Key_field]['nuclear_spin_frequencies']\n",
    "    freq_range=data_list[ii][0][Key_field]['readout_freqs']\n",
    "\n",
    "    detuned_electron_amplitude=data_list[ii][0][Key_field]['detuned_electron_amplitude_a']\n",
    "    detuned_sideband_amplitude=0.15\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "    fig,ax=plt.subplots(2,1,figsize=(12,9))\n",
    "    sigmoid_length = 5\n",
    "    x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "    ############################### extract populations #################################\n",
    "\n",
    "    p_data=(data>120)\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    pops_all.append(pops)\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    for i in range(len(freq_range)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[0].legend()\n",
    "    ax[0].set_title(f'Allowed power : {detuned_electron_amplitude:.2f} , Sideband power : {detuned_sideband_amplitude:.2f}')\n",
    "\n",
    "    bins=np.arange(50,np.max(np.concatenate(data)),5)\n",
    "    ax[1].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "    ax[1].legend()\n",
    "    ax[1].set_ylabel(\"instances\")\n",
    "    ax[1].set_xlabel('counts')\n",
    "\n",
    "    counts = np.array([x, *list(data.mean(0).T)]).T\n",
    "    pop_ = np.array([x, *pops]).T\n",
    "    \n",
    "    np.savetxt(f'data/counts_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.txt',counts)        \n",
    "    np.savetxt(f'data/pops_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.txt',pop_)  \n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'data/spectrocsopy_plot_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.pdf')\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f3b74f4-a521-4ac4-a23f-5d558fda2066",
   "metadata": {},
   "outputs": [],
   "source": [
    "sigmoid_length = 5\n",
    "nuclear_spin_frequencies=data_list[0][0][Key_field]['nuclear_spin_frequencies']\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "plot_guess = 0\n",
    "############################### extract populations #################################\n",
    "p_data = []\n",
    "states_to_fit = [3,1]\n",
    "xs =  []\n",
    "fit= []\n",
    "for i in range(2):\n",
    "    data=data_list[i][0][Key_field]['click_array']\n",
    "    nuclear_spin_frequencies=data_list[i][0][Key_field]['nuclear_spin_frequencies']\n",
    "    xs.append(1e-3*np.array(nuclear_spin_frequencies))\n",
    "    if len(readout_freqs) == 4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        p_data.append(pops[states_to_fit[i]])\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    ############################### Fit ###############################\n",
    "\n",
    "    guess = [xs[-1][np.argmin(p_data[-1])],(max(xs[-1])-min(xs[-1]))/8, min(p_data[-1])-max(p_data[-1]), max(p_data[-1])]\n",
    "    try: \n",
    "        fit.append(fit_function(guess, lorentz, xs[-1], p_data[-1]))\n",
    "        fit_success = 1\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        fit_success = 0\n",
    "        fine = np.linspace(xs[-1][0],xs[-1][-1],len(xs[-1])*100)\n",
    "\n",
    "pulse_bandwidth = 1/(int(5.44e6//4)*4*1e-9)\n",
    "\n",
    "amplitude = 1\n",
    "cos_ramp_time = int(0.4e6/4)\n",
    "top_wait_time = int(5.44e6//4)\n",
    "center_freq = int(810.471e3)\n",
    "around_pulse = 10e6\n",
    "vals = np.concatenate(([0]*int(around_pulse/4),chirp_cos_raise(cos_ramp_time,amplitude,0)[0], [amplitude]*top_wait_time, chirp_cos_raise(cos_ramp_time,-amplitude,0)[0]+amplitude,[0]*int(around_pulse/4)))\n",
    "times = np.linspace(0,len(vals)*4*1e-6,len(vals))\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(times),d=times[1]-times[0])\n",
    "fft_y = np.abs(np.fft.rfft(vals))\n",
    "full_pulse = np.concatenate((np.flip(fft_y),fft_y))\n",
    "full_freq = np.concatenate((-np.flip(fft_x),fft_x))\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.ylim(0,1)\n",
    "plt.xlim(xs[0][0],xs[0][-1])\n",
    "plt.xlabel('Nuclear drive frequency (kHz)')\n",
    "plt.ylabel(\"Population\")\n",
    "# plt.plot(xs[0], p_data[0], \"o\", label = labels[states_to_fit[0]],color= colors[states_to_fit[0]]) \n",
    "# plt.plot(xs[1], p_data[1], \"o\", label = labels[states_to_fit[1]],color= colors[states_to_fit[1]])\n",
    "plt.plot((full_freq+center_freq)/1e3,full_pulse/(5*max(full_pulse)),\"k--\")\n",
    "for i in range(2):\n",
    "    plt.plot(xs[i], p_data[i], \"o\", label = labels[states_to_fit[i]],color= colors[states_to_fit[i]]) \n",
    "    plt.plot(fit[i][2],fit[i][3], label = f\"centre freq = {np.round(fit[i][0][0],3)} kHz $\\sigma$ = {np.round(fit[i][0][1],2)} kHz\",color = colors[states_to_fit[i]])\n",
    "if plot_guess: plt.plot(fine,lorentz(fine,*guess))\n",
    "\n",
    "plt.legend(fontsize = \"small\", loc = \"upper right\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d809197-b632-4c51-ac9a-a567655aa27e",
   "metadata": {},
   "source": [
    "## Sweep electron amplitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b42feaf3-215d-4a68-9041-cc6259b5ed05",
   "metadata": {},
   "outputs": [],
   "source": [
    "path='Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_spectroscopy'\n",
    "timestamp_list = grab_timestamps_hdf(path, \"20240220152339\",\"20240220164756\")\n",
    "data_list = load_from_directory(path, timestamp_list)\n",
    "def lorentz(delta,delta0,kappa,a,b):\n",
    "    return a/(1+(delta-delta0)**2/(kappa/2)**2)+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4608639-0588-4c46-b697-6d80f1f1d99f",
   "metadata": {},
   "outputs": [],
   "source": [
    "lorentz_func = lambda x,*y: y[0]/((x-y[2])**2+y[1]**2)+y[3]\n",
    "Key_field='0'\n",
    "\n",
    "pops_all = []\n",
    "\n",
    "for ii in range(len(data_list)):\n",
    "    data=data_list[ii][0][Key_field]['click_array']\n",
    "    nuclear_spin_frequencies=data_list[ii][0][Key_field]['nuclear_spin_frequencies']\n",
    "    freq_range=data_list[ii][0][Key_field]['readout_freqs']\n",
    "\n",
    "    detuned_electron_amplitude=data_list[ii][0][Key_field]['detuned_electron_amplitude_a']\n",
    "    detuned_sideband_amplitude=0.15\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "    fig,ax=plt.subplots(2,1,figsize=(12,9))\n",
    "    sigmoid_length = 5\n",
    "    x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "    ############################### extract populations #################################\n",
    "\n",
    "    p_data=(data>120)\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    pops_all.append(pops)\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    for i in range(len(freq_range)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[0].legend()\n",
    "    ax[0].set_title(f'Allowed power : {detuned_electron_amplitude:.2f} , Sideband power : {detuned_sideband_amplitude:.2f}')\n",
    "\n",
    "    bins=np.arange(50,np.max(np.concatenate(data)),5)\n",
    "    ax[1].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "    ax[1].legend()\n",
    "    ax[1].set_ylabel(\"instances\")\n",
    "    ax[1].set_xlabel('counts')\n",
    "\n",
    "    counts = np.array([x, *list(data.mean(0).T)]).T\n",
    "    pop_ = np.array([x, *pops]).T\n",
    "    \n",
    "    np.savetxt(f'data/counts_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.txt',counts)        \n",
    "    np.savetxt(f'data/pops_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.txt',pop_)  \n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'data/spectrocsopy_plot_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.pdf')\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e509cf40-5f30-4734-9c2d-cab68a6851d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "est_list[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f17ab38-be04-4512-ac5e-66efd16e51bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "est_list = []\n",
    "for n, pop in enumerate(pops_all):\n",
    "    idx = -1\n",
    "    p_data = pop[idx]\n",
    "    plt.plot(x, p_data)\n",
    "    guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "    try: \n",
    "        est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "        fit_success = 1\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        fit_success = 0\n",
    "        fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "    est_list.append(est)\n",
    "    plt.plot(fine,data_fit,color='gray')\n",
    "\n",
    "est_list = np.array(est_list)\n",
    "plt.figure()\n",
    "rabi_freq_kHz = (2*raman_pi_duration_a_prep*4e-6)\n",
    "factors = np.linspace(1,0.5,6) * 1 / rabi_freq_kHz\n",
    "delta = (est_list[:,0]-est_list[0,0])*1e3\n",
    "\n",
    "plt.plot(factors, delta, 'o-')\n",
    "plt.xlabel(r'$\\Omega_{R} / 2\\pi$ (kHz)')\n",
    "plt.ylabel('Shift (Hz)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acb7feec-d52c-4421-b2b4-a65c92e37c5b",
   "metadata": {},
   "source": [
    "## Sweep sideband amplitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b941e6de-f093-424d-a96a-6f7678bc0553",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3\\\\raman_spectroscopy\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = [\n",
    "    '20231204160206_\\\\', \n",
    "    '20231204173255_\\\\', \n",
    "    '20231204190347_\\\\', \n",
    "    '20231204203439_\\\\', \n",
    "    '20231204220532_\\\\', \n",
    "    '20231204233625_\\\\', \n",
    "    '20231205010718_\\\\', \n",
    "    '20231205023811_\\\\', \n",
    "    '20231205040905_\\\\', \n",
    "    '20231205053958_\\\\', \n",
    "]\n",
    "\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    directory=os.listdir(path+timestamp)[0]\n",
    "    filename=getfiles_hdf5(path+timestamp+directory)\n",
    "    data=load_h5_to_dic(path+timestamp+directory+r'\\\\'+filename[0])\n",
    "    data_list.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47d2fa99-8060-4854-9a48-1c1e6b6c0a96",
   "metadata": {},
   "outputs": [],
   "source": [
    "lorentz_func = lambda x,*y: y[0]/((x-y[2])**2+y[1]**2)+y[3]\n",
    "Key_field='0'\n",
    "\n",
    "amps = np.linspace(0.01,0.30,10)\n",
    "for ii in range(len(data_list)):\n",
    "    data=data_list[ii][0][Key_field]['click_array']\n",
    "    nuclear_spin_frequencies=data_list[ii][0][Key_field]['nuclear_spin_frequencies']\n",
    "    freq_range=data_list[ii][0][Key_field]['readout_freqs']\n",
    "\n",
    "    detuned_electron_amplitude=data_list[ii][0][Key_field]['detuned_electron_amplitude_a']\n",
    "    detuned_sideband_amplitude= amps[ii]\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "    fig,ax=plt.subplots(2,1,figsize=(12,9))\n",
    "    sigmoid_length = 5\n",
    "    x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "    ############################### extract populations #################################\n",
    "\n",
    "    p_data=(data>120)\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    for i in range(len(freq_range)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[0].legend()\n",
    "    ax[0].set_title(f'Allowed power : {detuned_electron_amplitude:.2f} , Sideband power : {detuned_sideband_amplitude:.2f}')\n",
    "\n",
    "    bins=np.arange(50,np.max(np.concatenate(data)),5)\n",
    "    ax[1].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "    ax[1].legend()\n",
    "    ax[1].set_ylabel(\"instances\")\n",
    "    ax[1].set_xlabel('counts')\n",
    "\n",
    "    counts = np.array([x, *list(data.mean(0).T)]).T\n",
    "    pop_ = np.array([x, *pops]).T\n",
    "    \n",
    "    np.savetxt(f'data/counts_sweep_nuclear_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.txt',counts)        \n",
    "    np.savetxt(f'data/pops_sweep_nuclear_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.txt',pop_)  \n",
    "    \n",
    "    plt.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b59de7f7-2297-4bdc-89ee-70e3675293d1",
   "metadata": {},
   "source": [
    "## Stark Shift with alternating b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8aabbf8-161c-47b1-a83f-aa0442fcd251",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_spectroscopy\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = [\n",
    "    '20240202235747', \n",
    "    '20240203113233', \n",
    "]\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    filenames=getfiles_hdf5(path)\n",
    "    for filename in filenames:\n",
    "        if timestamp in filename: \n",
    "            data=load_h5_to_dic(path+r'\\\\'+filename)\n",
    "            data_list.append(data)\n",
    "    \n",
    "def lorentz(delta,delta0,kappa,a,b):\n",
    "    return a/(1+(delta-delta0)**2/(kappa/2)**2)+b\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c268941b-0f08-4d54-9bb4-24b7d83cd498",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3\\\\raman_spectroscopy\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = [\n",
    "    '20231210134709_\\\\', \n",
    "    '20231209193310_\\\\', \n",
    "]\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    directory=os.listdir(path+timestamp)[0]\n",
    "    filename=getfiles_hdf5(path+timestamp+directory)\n",
    "    data=load_h5_to_dic(path+timestamp+directory+r'\\\\'+filename[0])\n",
    "    data_list.append(data)\n",
    "    \n",
    "def lorentz(delta,delta0,kappa,a,b):\n",
    "    return a/(1+(delta-delta0)**2/(kappa/2)**2)+b\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb9d1da0-c764-4004-865b-a33fbc7af18f",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5ef4f64-8622-4dc9-b3cb-ff2a0d2769af",
   "metadata": {},
   "outputs": [],
   "source": [
    "lorentz_func = lambda x,*y: y[0]/((x-y[2])**2+y[1]**2)+y[3]\n",
    "Key_field='0'\n",
    "p=[-1,-2]\n",
    "l = [r\"${|\\downarrow\\rangle_B}$\", r\"${|\\uparrow\\rangle_B}$\"]\n",
    "c = ['b', 'orange']\n",
    "fig,ax=plt.subplots(1,1,figsize=(8,5))\n",
    "ax.set_ylabel(r\"P$^{a}_{|\\downarrow\\rangle}$\")\n",
    "ax.set_xlabel('Nuclear drive frequency - 808.383 kHz (Hz)')\n",
    "\n",
    "for ii in range(len(data_list)):\n",
    "    data=data_list[ii][0][Key_field]['click_array']\n",
    "    nuclear_spin_frequencies=data_list[ii][0][Key_field]['nuclear_spin_frequencies']\n",
    "    freq_range=data_list[ii][0][Key_field]['readout_freqs']\n",
    "\n",
    "    detuned_electron_amplitude=data_list[ii][0][Key_field]['detuned_electron_amplitude_a']\n",
    "    detuned_sideband_amplitude=0.15\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "    sigmoid_length = 5\n",
    "    x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "    ############################### extract populations #################################\n",
    "    \n",
    "    p_data=(data>75)\n",
    "    y = p_data[:,:,p[ii]]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "\n",
    "    guess = [x[np.argmin(y.mean(0))],(max(x)-min(x))/4,-1,1]\n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, y.mean(0))\n",
    "    ax.errorbar((x-808.383)*1e3, y.mean(0), y.std(0)/np.sqrt(len(data)), fmt = \"o\", color=c[ii]) \n",
    "    ax.plot((fine-808.383)*1e3,data_fit, label = \"%s | f = %.3f kHz | $\\delta$ = %.3f kHz\"%(l[ii], est[0], est[1]), color=c[ii])\n",
    "    ax.legend()\n",
    "    ax.set_title('Spectroscopy of spin A')\n",
    "    plt.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "621bb31c-c572-4fe9-a4cb-cb6d71cc1cb8",
   "metadata": {},
   "source": [
    "## Stark Shift with alternating a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a97ab81-67bb-4c9e-8e92-3bd22f7403bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_spectroscopy\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = [\n",
    "    \n",
    "    '20240215111639', \n",
    "    '20240215105246', \n",
    "]\n",
    "\n",
    "#    '20231211161925', \n",
    "#    '20231211182727', \n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    filenames=getfiles_hdf5(path)\n",
    "    for filename in filenames:\n",
    "        if timestamp in filename: \n",
    "            data=load_h5_to_dic(path+r'\\\\'+filename)\n",
    "            data_list.append(data)\n",
    "    \n",
    "def lorentz(delta,delta0,kappa,a,b):\n",
    "    return a/(1+(delta-delta0)**2/(kappa/2)**2)+b\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba7cabc6-f7fc-44ab-9cc4-0ac408f50429",
   "metadata": {},
   "outputs": [],
   "source": [
    "lorentz_func = lambda x,*y: y[0]/((x-y[2])**2+y[1]**2)+y[3]\n",
    "Key_field='0'\n",
    "p=[3,1]\n",
    "l = [r\"${|\\downarrow\\rangle_A}$\", r\"${|\\uparrow\\rangle_A}$\"]\n",
    "c = ['b', 'orange']\n",
    "fig,ax=plt.subplots(1,1,figsize=(8,5))\n",
    "ax.set_ylabel(r\"P$^{a}_{|\\downarrow\\rangle}$\")\n",
    "ax.set_xlabel('Nuclear drive frequency - 810.467 kHz (Hz)')\n",
    "\n",
    "for ii in range(len(data_list)):\n",
    "    data=data_list[ii][0][Key_field]['click_array']\n",
    "    nuclear_spin_frequencies=data_list[ii][0][Key_field]['nuclear_spin_frequencies']\n",
    "    freq_range=data_list[ii][0][Key_field]['readout_freqs']\n",
    "\n",
    "    detuned_electron_amplitude=data_list[ii][0][Key_field]['detuned_electron_amplitude_a']\n",
    "    detuned_sideband_amplitude=0.15\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "    sigmoid_length = 5\n",
    "    x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "    ############################### extract populations #################################\n",
    "    \n",
    "    p_data=(data>75)\n",
    "    y = p_data[:,:,p[ii]]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "\n",
    "    guess = [x[np.argmin(y.mean(0))],(max(x)-min(x))/4,-1,1]\n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, y.mean(0))\n",
    "    ax.errorbar((x-810.467)*1e3, y.mean(0), y.std(0)/np.sqrt(len(data)), fmt = \"o\", color=c[ii]) \n",
    "    ax.plot((fine-810.467)*1e3,data_fit, label = \"%s | f = %.3f kHz | $\\delta$ = %.3f kHz\"%(l[ii], est[0], est[1]), color=c[ii])\n",
    "    ax.legend()\n",
    "    ax.set_title('Spectroscopy of spin B')\n",
    "    plt.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8deb103b-93f9-40c9-88f6-8a8309a26e1a",
   "metadata": {},
   "source": [
    "## Both amplitudes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8de33d7b-c33c-4bd2-ada9-38ff3bc0d405",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_spectroscopy\\\\'\n",
    "timestamp_list = [\n",
    "    '20240109230523', \n",
    "    '20240109233536', \n",
    "    '20240110000926',\n",
    "    '20240110151548',\n",
    "    '20240110154601'\n",
    "]\n",
    "\n",
    "#    '20231211161925', \n",
    "#    '20231211182727', \n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    filenames=getfiles_hdf5(path)\n",
    "    for filename in filenames:\n",
    "        if timestamp in filename: \n",
    "            data=load_h5_to_dic(path+r'\\\\'+filename)\n",
    "            data_list.append(data)\n",
    "    \n",
    "def lorentz(delta,delta0,kappa,a,b):\n",
    "    return a/(1+(delta-delta0)**2/(kappa/2)**2)+b\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e8810bb-1dc8-493d-8307-97a018e9279f",
   "metadata": {},
   "outputs": [],
   "source": [
    "lorentz_func = lambda x,*y: y[0]/((x-y[2])**2+y[1]**2)+y[3]\n",
    "Key_field='0'\n",
    "p=[-1,-1]\n",
    "fig,ax=plt.subplots(1,1,figsize=(8,5))\n",
    "ax.set_ylabel(r\"P$^{a}_{|\\downarrow\\rangle}$\")\n",
    "ax.set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax.set_title('Spectroscopy as a function of power, Spin A')\n",
    "\n",
    "factors = np.array([1, 0.89285714, 0.78571429, 0.6, 0.4])\n",
    "est_array = []\n",
    "for ii in range(len(data_list)):\n",
    "    data=data_list[ii][0][Key_field]['click_array']\n",
    "    nuclear_spin_frequencies=data_list[ii][0][Key_field]['nuclear_spin_frequencies']\n",
    "    freq_range=data_list[ii][0][Key_field]['readout_freqs']\n",
    "\n",
    "    detuned_electron_amplitude=data_list[ii][0][Key_field]['detuned_electron_amplitude_a']\n",
    "    detuned_sideband_amplitude=0.15\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "    sigmoid_length = 5\n",
    "    x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "    ############################### extract populations #################################\n",
    "    \n",
    "    p_data=(data>85)\n",
    "    y = p_data[:,:,0]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "\n",
    "    guess = [x[np.argmin(y.mean(0))],(max(x)-min(x))/4,-1,1]\n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, y.mean(0))\n",
    "    ax.errorbar((x), y.mean(0), y.std(0)/np.sqrt(len(data)), fmt = \"o\", color=colors[ii]) \n",
    "    ax.plot((fine),data_fit, label = \"f = %.3f kHz | $\\delta$ = %.3f kHz\"%(est[0], est[1]), color=colors[ii], alpha=0.3)\n",
    "    ax.set_ylim([0, 1.1])\n",
    "    ax.legend()\n",
    "    plt.tight_layout()\n",
    "    est_array.append(est)\n",
    "\n",
    "\n",
    "est_array = np.array(est_array)\n",
    "\n",
    "x = factors**2\n",
    "f = est_array[:,0]*1e3\n",
    "vals = np.polyfit(x, f, 1)\n",
    "x_fit = np.linspace(0,1, 10)\n",
    "y_0 = np.polyval(vals,0)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(x, f-y_0, 'o', label='Measurement')\n",
    "plt.plot(x_fit, np.polyval(vals,x_fit)-y_0)\n",
    "plt.xlabel('Proportionality factor squared')\n",
    "plt.ylabel(r'$\\Delta^{diff}_{AC}$ (Hz)')\n",
    "plt.title(r'$\\delta f$ = %.1f Hz'%(np.polyval(vals,x_fit[-1])-y_0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4595057-4c0f-4a61-a2ba-f2c5f4e6b603",
   "metadata": {},
   "source": [
    "## Vs detuning (interleaved)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb1ba7de-84a0-492f-9800-a048cfd00904",
   "metadata": {},
   "source": [
    "### On A (B interleaved)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb8cad69-bcdb-41cc-96c4-c48652fae11a",
   "metadata": {},
   "outputs": [],
   "source": [
    "path='Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_spectroscopy'\n",
    "# timestamp_list = grab_timestamps_hdf(path,\"20240223114442\",\"20240223125540\")\n",
    "# timestamp_list+= grab_timestamps_hdf(path,\"20240222004959\",\"20240222073419\")\n",
    "# timestamp_list = grab_timestamps_hdf(path,\"20240224130838\", \"20240224154025\")\n",
    "# timestamp_list  = grab_timestamps_hdf(path,\"20240224171133\",\"20240224191244\")\n",
    "# timestamp_list  = grab_timestamps_hdf(path,\"20240224202030\",\"20240224222137\")\n",
    "# timestamp_list = grab_timestamps_hdf(path,\"20240302085235\",\"20240302085235\")\n",
    "# timestamp_list = grab_timestamps_hdf(path,\"20240312123145\",\"20240312133215\")\n",
    "# timestamp_list.append(\"20240312155138\")\n",
    "# timestamp_list.append(\"20240312160644\")\n",
    "# timestamp_list.append(\"20240312165444\")\n",
    "# timestamp_list.append(\"20240312170950\")\n",
    "timestamp_list = grab_timestamps_hdf(path,\"20240312185110\",\"20240313005302\")\n",
    "\n",
    "data_list = load_from_directory(path, timestamp_list)\n",
    "def lorentz(delta,delta0,kappa,a,b):\n",
    "    return a/(1+(delta-delta0)**2/(kappa/2)**2)+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59387516-34a3-4b7b-a477-665143da1374",
   "metadata": {},
   "outputs": [],
   "source": [
    "raman_detuning = -int(809.791e3/2-10e3)\n",
    "raman_detunings = raman_detuning+ sinhspace(-125e3,25e3,7, nonlinearity = 2)\n",
    "# raman_detunings = np.append(raman_detunings,raman_detuning-60e3)\n",
    "# raman_detunings = np.append(raman_detunings,raman_detuning-45e3)\n",
    "# raman_detunings = np.append(raman_detunings,raman_detuning-120e3)\n",
    "# raman_detunings = np.append(raman_detunings,raman_detuning-90e3)\n",
    "raman_detunings = raman_detunings[:len(data_list)]\n",
    "\n",
    "print(raman_detunings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4896587e-3e52-4fcf-b50a-f9ab77b2698a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "lorentz_func = lambda x,*y: y[0]/((x-y[2])**2+y[1]**2)+y[3]\n",
    "Key_field='0'\n",
    "\n",
    "pops_all_flipped = []\n",
    "pops_all_unflipped = []\n",
    "\n",
    "for ii in range(len(data_list)):\n",
    "    for jj in range(2):\n",
    "        data=data_list[ii][0][Key_field]['click_array'][:,jj,:,:]\n",
    "        nuclear_spin_frequencies=data_list[ii][0][Key_field]['nuclear_spin_frequencies']\n",
    "        freq_range=data_list[ii][0][Key_field]['readout_freqs']\n",
    "    \n",
    "        detuned_electron_amplitude=data_list[ii][0][Key_field]['detuned_electron_amplitude_a']\n",
    "        detuned_sideband_amplitude=0.15\n",
    "    \n",
    "        ############################### Plotting ###############################\n",
    "        fig,ax=plt.subplots(2,1,figsize=(12,9))\n",
    "        sigmoid_length = 5\n",
    "        x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "        ############################### extract populations #################################\n",
    "    \n",
    "        \n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "        p_data=p3\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        if jj ==0: pops_all_flipped.append(pops)\n",
    "        else: pops_all_unflipped.append(pops)\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "        for i in range(len(freq_range)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "        ax[0].set_ylabel(\"Mean counts\")\n",
    "        ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "        ax[0].legend()\n",
    "        ax[0].set_title(f'Allowed power : {detuned_electron_amplitude:.2f} , Sideband power : {detuned_sideband_amplitude:.2f}')\n",
    "    \n",
    "        bins=np.arange(50,np.max(np.concatenate(data)),5)\n",
    "        ax[1].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "        ax[1].legend()\n",
    "        ax[1].set_ylabel(\"instances\")\n",
    "        ax[1].set_xlabel('counts')\n",
    "    \n",
    "        # counts = np.array([x, *list(data.mean(0).T)]).T\n",
    "        # pop_ = np.array([x, *pops]).T\n",
    "        # \n",
    "        # np.savetxt(f'data/counts_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.txt',counts)        \n",
    "        # np.savetxt(f'data/pops_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.txt',pop_)  \n",
    "        plt.tight_layout()\n",
    "        states = [\"down\",\"up\"]\n",
    "        plt.savefig(path+\"\\\\%s_raman_a_spectroscopy_b_%s.pdf\"%(timestamp_list[ii],states[jj]))\n",
    "        # plt.savefig(f'data/spectrocsopy_plot_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.pdf')\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c816b47-64a3-49ac-9429-32e31d2345c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "est_list_flipped = []\n",
    "est_list_unflipped = []\n",
    "std_list_flipped = []\n",
    "std_list_unflipped = []\n",
    "\n",
    "for jj in range(2):\n",
    "    if jj == 0:pops_all = pops_all_flipped\n",
    "    else: pops_all = pops_all_unflipped\n",
    "    for n, pop in enumerate(pops_all):\n",
    "        idx = 2+jj\n",
    "        p_data = pop[idx]\n",
    "        #plt.plot(x, p_data)\n",
    "        guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "        try: \n",
    "            est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "            fit_success = 1\n",
    "        except: \n",
    "            print(\"fit failed\")\n",
    "            fit_success = 0\n",
    "            fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "            data_fit = np.zeros_like(fine)\n",
    "        if jj == 0: \n",
    "            est_list_flipped.append(est)\n",
    "            std_list_flipped.append(std)\n",
    "        else: \n",
    "            est_list_unflipped.append(est)\n",
    "            std_list_unflipped.append(std)\n",
    "            \n",
    "        plt.plot(x,p_data, \"o\", alpha = 0.5,color=colors[n])\n",
    "        plt.plot(fine,data_fit, alpha = 0.5,color=colors[n])\n",
    "    \n",
    "est_list_flipped   = np.array(est_list_flipped)\n",
    "est_list_unflipped = np.array(est_list_unflipped)\n",
    "std_list_flipped   = np.array(std_list_flipped)\n",
    "std_list_unflipped = np.array(std_list_unflipped)\n",
    "\n",
    "#################################\n",
    "lin_func = lambda x,*y: y[0]*x+y[1]\n",
    "\n",
    "x = 1e-3*(raman_detunings-raman_detuning)\n",
    "y = 1e3*(est_list_flipped[:,0]-est_list_unflipped[:,0])\n",
    "guess = [-2,-20]\n",
    "\n",
    "est,std,fine,data_fit = fit_function(guess, lin_func, x, y)\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "# plt.plot(1e-3*(raman_detunings-raman_detuning),1e3*(est_list_flipped[:,0]-est_list_unflipped[:,0]), 'o')\n",
    "\n",
    "plt.errorbar(x,y, yerr = 1e3*(std_list_flipped[:,0]+std_list_unflipped[:,0]),\n",
    "            fmt='o', markersize=8, capsize=10)\n",
    "plt.plot(fine,data_fit, label = \"$\\Delta_\\mathrm{optimal} = %i$ kHz\"%(-est[1]/est[0]))\n",
    "plt.axvline(-est[1]/est[0],linestyle = \"--\", alpha = 0.5, color = \"k\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"$\\Delta_\\mathrm{Raman~detuning}$ (kHz)\")\n",
    "plt.ylabel('Conditional shift (Hz)')\n",
    "plt.grid()\n",
    "plt.tight_layout()\n",
    "plt.savefig(path+\"\\\\%s-%s_raman_b_spectroscopy_a_delta_comparison.pdf\"%(timestamp_list[0],timestamp_list[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5effe34-ef4c-4b67-8015-2ff0c3b5b0f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "est_list_flipped = []\n",
    "est_list_unflipped = []\n",
    "for jj in range(2):\n",
    "    if jj == 0:pops_all = pops_all_flipped\n",
    "    else: pops_all = pops_all_unflipped\n",
    "    for n, pop in enumerate(pops_all):\n",
    "        idx = 2+jj\n",
    "        p_data = pop[idx]\n",
    "        #plt.plot(x, p_data)\n",
    "        guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "        try: \n",
    "            est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "            fit_success = 1\n",
    "        except: \n",
    "            print(\"fit failed\")\n",
    "            fit_success = 0\n",
    "            fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "            data_fit = np.zeros_like(fine)\n",
    "        if jj == 0: est_list_flipped.append(est)\n",
    "        else: est_list_unflipped.append(est)\n",
    "        plt.plot(x,p_data)\n",
    "        plt.plot(fine,data_fit)#,color='gray')\n",
    "    \n",
    "est_list_flipped = np.array(est_list_flipped)\n",
    "est_list_unflipped = np.array(est_list_unflipped)\n",
    "plt.figure()\n",
    "plt.plot(1e-3*(raman_detunings-raman_detuning),1e3*(est_list_flipped[:,0]-est_list_unflipped[:,0]), 'o')\n",
    "\n",
    "plt.xlabel(\"$\\Delta_\\mathrm{Raman~detuning}$ (kHz)\")\n",
    "plt.ylabel('Conditional shift (Hz)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bafc909-8a3b-46be-a094-a6a05d59dca1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T09:35:37.630638Z",
     "iopub.status.busy": "2024-02-26T09:35:37.629640Z",
     "iopub.status.idle": "2024-02-26T09:35:37.652640Z",
     "shell.execute_reply": "2024-02-26T09:35:37.651639Z",
     "shell.execute_reply.started": "2024-02-26T09:35:37.630638Z"
    }
   },
   "source": [
    "### On B (A interleaved)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32133dd7-5b14-4a88-9c8c-2020a06ba5a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "path='Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_spectroscopy'\n",
    "\n",
    "# timestamp_list = grab_timestamps_hdf(path,\"20240301213422\",\"20240301230459\")\n",
    "timestamp_list = grab_timestamps_hdf(path,\"20240313015318\",\"20240313075455\")\n",
    "#timestamp_list.append(\"20240312172457\")\n",
    "#timestamp_list.append(\"20240312174003\")\n",
    "data_list = load_from_directory(path, timestamp_list)\n",
    "def lorentz(delta,delta0,kappa,a,b):\n",
    "    return a/(1+(delta-delta0)**2/(kappa/2)**2)+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e4f8810-3106-4d31-b418-5a1406cf08cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "raman_detuning = -int(809.791e3/2-10e3)\n",
    "# raman_detunings = raman_detuning+ np.linspace(-30e3,30e3,5)\n",
    "# raman_detunings = np.append(raman_detunings,raman_detuning-45e3)\n",
    "# raman_detunings = np.append(raman_detunings,raman_detuning+45e3)\n",
    "\n",
    "raman_detunings = raman_detuning+ sinhspace(-50e3,50e3,7,nonlinearity=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27f889ea-67ac-491b-b4bc-9caad4d6d9c4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "lorentz_func = lambda x,*y: y[0]/((x-y[2])**2+y[1]**2)+y[3]\n",
    "Key_field='0'\n",
    "\n",
    "pops_all_flipped = []\n",
    "pops_all_unflipped = []\n",
    "\n",
    "for ii in range(len(data_list)):\n",
    "    for jj in range(2):\n",
    "        data=data_list[ii][0][Key_field]['click_array'][:,jj,:,:]\n",
    "        nuclear_spin_frequencies=data_list[ii][0][Key_field]['nuclear_spin_frequencies']\n",
    "        freq_range=data_list[ii][0][Key_field]['readout_freqs']\n",
    "    \n",
    "        detuned_electron_amplitude=data_list[ii][0][Key_field]['detuned_electron_amplitude_a']\n",
    "        detuned_sideband_amplitude=0.15\n",
    "    \n",
    "        ############################### Plotting ###############################\n",
    "        fig,ax=plt.subplots(2,1,figsize=(12,9))\n",
    "        sigmoid_length = 5\n",
    "        x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "        ############################### extract populations #################################\n",
    "    \n",
    "        \n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "        p_data=p3\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        if jj ==0: pops_all_flipped.append(pops)\n",
    "        else: pops_all_unflipped.append(pops)\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "        for i in range(len(freq_range)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "        ax[0].set_ylabel(\"Mean counts\")\n",
    "        ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "        ax[0].legend()\n",
    "        ax[0].set_title(f'Allowed power : {detuned_electron_amplitude:.2f} , Sideband power : {detuned_sideband_amplitude:.2f}')\n",
    "    \n",
    "        bins=np.arange(50,np.max(np.concatenate(data)),5)\n",
    "        ax[1].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "        ax[1].legend()\n",
    "        ax[1].set_ylabel(\"instances\")\n",
    "        ax[1].set_xlabel('counts')\n",
    "    \n",
    "        # counts = np.array([x, *list(data.mean(0).T)]).T\n",
    "        # pop_ = np.array([x, *pops]).T\n",
    "        # \n",
    "        # np.savetxt(f'data/counts_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.txt',counts)        \n",
    "        # np.savetxt(f'data/pops_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.txt',pop_)  \n",
    "        plt.tight_layout()\n",
    "        states = [\"down\",\"up\"]\n",
    "        plt.savefig(path+\"\\\\%s_raman_b_spectroscopy_a_%s.pdf\"%(timestamp_list[ii],states[jj]))\n",
    "        # plt.savefig(f'data/spectrocsopy_plot_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.pdf')\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbb6dcb2-b650-4a22-a416-14ac1a98080f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "est_list_flipped = []\n",
    "est_list_unflipped = []\n",
    "std_list_flipped = []\n",
    "std_list_unflipped = []\n",
    "\n",
    "for jj in range(2):\n",
    "    if jj == 0:pops_all = pops_all_flipped\n",
    "    else: pops_all = pops_all_unflipped\n",
    "    for n, pop in enumerate(pops_all):\n",
    "        idx = 1+2*jj\n",
    "        p_data = pop[idx]\n",
    "        #plt.plot(x, p_data)\n",
    "        guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "        try: \n",
    "            est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "            fit_success = 1\n",
    "        except: \n",
    "            print(\"fit failed\")\n",
    "            fit_success = 0\n",
    "            fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "            data_fit = np.zeros_like(fine)\n",
    "        if jj == 0: \n",
    "            est_list_flipped.append(est)\n",
    "            std_list_flipped.append(std)\n",
    "        else: \n",
    "            est_list_unflipped.append(est)\n",
    "            std_list_unflipped.append(std)\n",
    "            \n",
    "        plt.plot(x,p_data)\n",
    "        plt.plot(fine,data_fit)#,color='gray')\n",
    "    \n",
    "est_list_flipped   = np.array(est_list_flipped)\n",
    "est_list_unflipped = np.array(est_list_unflipped)\n",
    "std_list_flipped   = np.array(std_list_flipped)\n",
    "std_list_unflipped = np.array(std_list_unflipped)\n",
    "\n",
    "\n",
    "#################################\n",
    "lin_func = lambda x,*y: y[0]*x+y[1]\n",
    "\n",
    "x = 1e-3*(raman_detunings-raman_detuning)\n",
    "y = 1e3*(est_list_flipped[:,0]-est_list_unflipped[:,0])\n",
    "guess = [-2,-20]\n",
    "\n",
    "est,std,fine,data_fit = fit_function(guess, lin_func, x, y)\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "# plt.plot(1e-3*(raman_detunings-raman_detuning),1e3*(est_list_flipped[:,0]-est_list_unflipped[:,0]), 'o')\n",
    "\n",
    "plt.errorbar(x,y, yerr = 1e3*(std_list_flipped[:,0]+std_list_unflipped[:,0]),\n",
    "            fmt='o', markersize=8, capsize=10)\n",
    "plt.plot(fine,data_fit, label = \"$\\Delta_\\mathrm{optimal} = %i$ kHz\"%(-est[1]/est[0]))\n",
    "plt.axvline(-est[1]/est[0],linestyle = \"--\", alpha = 0.5, color = \"k\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"$\\Delta_\\mathrm{Raman~detuning}$ (kHz)\")\n",
    "plt.ylabel('Conditional shift (Hz)')\n",
    "plt.grid()\n",
    "plt.tight_layout()\n",
    "plt.savefig(path+\"\\\\%s-%s_raman_a_spectroscopy_delta_comparison.pdf\"%(timestamp_list[0],timestamp_list[-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a542c4f9-46b3-458b-b895-82ca1a17602f",
   "metadata": {},
   "source": [
    "## Quantro: 1, Error: Knill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "233e448e-77af-4638-94d8-dd39321267f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_spectroscopy\\\\'\n",
    "timestamp_list = [\"20240222230852\"]\n",
    "\n",
    "data_list = load_from_directory(path, timestamp_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f04e5cf1-0a4b-4d38-b79f-c48d380840aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data=data_list[0][0][Key_field]['click_array']\n",
    "nuclear_spin_frequencies = data_list[0][0][Key_field]['nuclear_spin_frequencies']\n",
    "\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(3,1,figsize=(12,12))\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "############################### extract populations #################################\n",
    "\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "p_data=p3\n",
    "pops = [p0,p1,p2,p3]\n",
    "pops_all.append(pops)\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "###############################\n",
    "for i in range(len(freq_range)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "ax[0].set_title(f'Allowed power : {detuned_electron_amplitude:.2f} , Sideband power : {detuned_sideband_amplitude:.2f}')\n",
    "\n",
    "for (k,pop) in enumerate(pops):\n",
    "    ############################### Fit ###############################\n",
    "    guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "    try: \n",
    "        est,std,fine,data_fit = fit_function(guess, lorentz, x, pop)\n",
    "        fit_success = 1\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        fit_success = 0\n",
    "        fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "    ###############################\n",
    "    plot_guess = 0\n",
    "    \n",
    "    if fit_success and k == 1 or k== 3:\n",
    "        ax[1].plot(fine,data_fit, color = cols[k])\n",
    "        ax[1].plot(x, pop, \"o\", label = labels[k]+f\", $f$ = {np.round(est[0],3)} kHz, $\\sigma$ = {np.round(est[1],2)} kHz\") \n",
    "    else: \n",
    "        ax[1].plot(x, pop, \"o\",label = labels[k])\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess), color = \"k\", alpha = 0.5)    \n",
    "ax[1].legend(fontsize = \"small\", loc = \"lower left\")\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].set_ylim(0,1)\n",
    "\n",
    "###############################\n",
    "bins=np.arange(50,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "###############################\n",
    "\n",
    "counts = np.array([x, *list(data.mean(0).T)]).T\n",
    "pop_ = np.array([x, *pops]).T\n",
    "\n",
    "# np.savetxt(f'data/counts_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.txt',counts)        \n",
    "# np.savetxt(f'data/pops_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.txt',pop_)  \n",
    "plt.tight_layout()\n",
    "# plt.savefig(f'data/spectrocsopy_plot_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab2f59f9-ffe3-46b4-beff-4fd8d04a63ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_red = pops[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3df26282-3bc1-4d56-966e-ec3cdf178d8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_spectroscopy\\\\'\n",
    "timestamp_list = [\"20240223031056\"]\n",
    "\n",
    "data_list = load_from_directory(path, timestamp_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e9d2726-e1d3-4ca5-8db3-9722d212aa19",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data=data_list[0][0][Key_field]['click_array']\n",
    "nuclear_spin_frequencies = data_list[0][0][Key_field]['nuclear_spin_frequencies']\n",
    "\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(3,1,figsize=(12,12))\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "############################### extract populations #################################\n",
    "\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "p_data=p2\n",
    "pops = [p0,p1,p2,p3]\n",
    "pops_all.append(pops)\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "###############################\n",
    "for i in range(len(freq_range)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "ax[0].set_title(f'Allowed power : {detuned_electron_amplitude:.2f} , Sideband power : {detuned_sideband_amplitude:.2f}')\n",
    "\n",
    "for (k,pop) in enumerate(pops):\n",
    "    ############################### Fit ###############################\n",
    "    guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "    try: \n",
    "        est,std,fine,data_fit = fit_function(guess, lorentz, x, pop)\n",
    "        fit_success = 1\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        fit_success = 0\n",
    "        fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "    ###############################\n",
    "    plot_guess = 0\n",
    "    \n",
    "    if fit_success and k == 0 or k== 2:\n",
    "        ax[1].plot(fine,data_fit, color = cols[k])\n",
    "        ax[1].plot(x, pop, \"o\", label = labels[k]+f\", $f$ = {np.round(est[0],3)} kHz, $\\sigma$ = {np.round(est[1],2)} kHz\") \n",
    "    else: \n",
    "        ax[1].plot(x, pop, \"o\",label = labels[k])\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess), color = \"k\", alpha = 0.5)    \n",
    "ax[1].legend(fontsize = \"small\", loc = \"lower left\")\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].set_ylim(0,1)\n",
    "\n",
    "###############################\n",
    "bins=np.arange(50,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "###############################\n",
    "\n",
    "counts = np.array([x, *list(data.mean(0).T)]).T\n",
    "pop_ = np.array([x, *pops]).T\n",
    "\n",
    "# np.savetxt(f'data/counts_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.txt',counts)        \n",
    "# np.savetxt(f'data/pops_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.txt',pop_)  \n",
    "plt.tight_layout()\n",
    "# plt.savefig(f'data/spectrocsopy_plot_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "450e90c5-c3c3-4df4-8489-751ce8425bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_green = pops[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45e069d6-a25c-4b96-8b2a-54d11c91b243",
   "metadata": {},
   "outputs": [],
   "source": [
    "est_red,std_red,fine_red,data_fit_red = fit_function(guess, lorentz, x, pop_red)\n",
    "est_green,std_green,fine_green,data_fit_green = fit_function(guess, lorentz, x, pop_green)\n",
    "\n",
    "plt.plot(fine_red,data_fit_red,  color = cols[0], label = labels[-1]+f\", $f$ = {np.round(est_red[0],3)} kHz, $\\sigma$ = {np.round(est_red[1],2)} kHz\")\n",
    "plt.plot(x,pop_red, \"o\", color = cols[0])\n",
    "plt.plot(fine_green,data_fit_green, color = cols[1], label = labels[-2]+f\", $f$ = {np.round(est_green[0],3)} kHz, $\\sigma$ = {np.round(est_green[1],2)} kHz\")\n",
    "plt.plot(x,pop_green, \"o\", color = cols[1])\n",
    "plt.ylim(0,1)\n",
    "plt.legend(fontsize = \"small\")\n",
    "plt.xlabel(\"Nuclear spin drive frequency (kHz)\")\n",
    "plt.ylabel(\"$P_\\mathrm{initial}$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a26fefa0-daab-489a-b558-ce13a7085892",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(x,pop_red, \"o-\", color = cols[0], label = labels[-1])\n",
    "plt.plot(x,pop_green, \"o-\", color = cols[1], label = labels[-2])\n",
    "plt.ylim(0,1)\n",
    "plt.legend(fontsize = \"small\")\n",
    "plt.xlabel(\"Nuclear spin drive frequency (kHz)\")\n",
    "plt.ylabel(\"$P_\\mathrm{initial}$\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83c093f7-e8d0-4953-be5f-62af958b2457",
   "metadata": {},
   "source": [
    "# CNOT Spectroscopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f111beb-623d-45c3-8ba6-0bd6688d2bd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_spectroscopy\\\\'\n",
    "timestamp_list = [\"20240311175326\"]\n",
    "\n",
    "data_list = load_from_directory(path, timestamp_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c269a4dd-e8e4-47fb-8195-f4895aaea847",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data=data_list[0][0][Key_field]['click_array']\n",
    "nuclear_spin_frequencies = data_list[0][0][Key_field]['nuclear_spin_frequencies']\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(2,1,figsize=(10,10))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],3)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "ax[1].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "counts = np.array([x, *list(data.mean(0).T)]).T\n",
    "pop_ = np.array([x, *pops]).T\n",
    "\n",
    "# np.savetxt(f'data/counts_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.txt',counts)        \n",
    "# np.savetxt(f'data/pops_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.txt',pop_)  \n",
    "plt.tight_layout()\n",
    "# plt.savefig(f'data/spectrocsopy_plot_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80bd657f-1b25-425e-96cb-9923d2de19e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_red = p_data\n",
    "x_red = x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f28ba390-176f-4887-92c1-52f85ee38d3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_spectroscopy\\\\'\n",
    "timestamp_list = [\"20240311225647\"]\n",
    "\n",
    "data_list = load_from_directory(path, timestamp_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f2e6cc4-8d12-4490-a77d-1c9a348ea9e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data=data_list[0][0][Key_field]['click_array']\n",
    "nuclear_spin_frequencies = data_list[0][0][Key_field]['nuclear_spin_frequencies']\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p1\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(2,1,figsize=(10,10))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],3)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "ax[1].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "counts = np.array([x, *list(data.mean(0).T)]).T\n",
    "pop_ = np.array([x, *pops]).T\n",
    "\n",
    "# np.savetxt(f'data/counts_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.txt',counts)        \n",
    "# np.savetxt(f'data/pops_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.txt',pop_)  \n",
    "plt.tight_layout()\n",
    "# plt.savefig(f'data/spectrocsopy_plot_sweep_electron_{detuned_electron_amplitude:.2f}_{detuned_sideband_amplitude:.2f}.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "960712b8-f49e-4a7c-9266-6b19a9ab2a25",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_orange = p_data\n",
    "x_orange = x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d60a4184-5ff2-479b-93d9-6b361d9f9cec",
   "metadata": {},
   "outputs": [],
   "source": [
    "est_red,std_red,fine_red,data_fit_red = fit_function([810.2,0.1, -1, 1], lorentz, x_red, pop_red)\n",
    "est_orange,std_orange,fine_orange,data_fit_orange = fit_function(guess, lorentz, x_orange, pop_orange)\n",
    "\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.plot(fine_red,data_fit_red,  color = cols[0], linewidth = 5, alpha = 0.6, label = r\"${|\\downarrow\\rangle}_A$\"+f\", $f_0$ = {np.round(est_red[0],3)} kHz\")\n",
    "plt.plot(x_red,pop_red, \"o\", color = cols[0])\n",
    "plt.plot(fine_orange,data_fit_orange, color = cols[1], linewidth = 5, alpha = 0.6, label = r\"${|\\uparrow\\rangle}_A$\"+f\", $f_0$ = {np.round(est_orange[0],3)} kHz\")\n",
    "plt.plot(x_orange,pop_orange, \"o\", color = cols[1])\n",
    "plt.ylim(0,1)\n",
    "plt.title('Conditional Raman Spectroscopy | $\\Delta f_0=$%.f Hz'%(1000*abs(np.round(est_red[0],3) - np.round(est_orange[0],3))))\n",
    "plt.legend(fontsize = \"small\", loc = 'lower right')\n",
    "plt.xlabel(\"Nuclear spin drive frequency (kHz)\")\n",
    "plt.ylabel(r\"$P_{|\\downarrow\\rangle,B}$\")\n",
    "plt.savefig('Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_spectroscopy\\\\b.png', dpi = 600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3aa7034-52f2-4f98-8aec-453fee6f88a9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fd759a5b-f3a5-4ead-a5e8-407aa4f2580b",
   "metadata": {},
   "source": [
    "# Echo with alternating b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d833dfd-f51c-472d-871f-0542999474cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3\\\\raman_echo_alternating\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = [\n",
    "    '20231207225733_\\\\', \n",
    "]\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    directory=os.listdir(path+timestamp)[0]\n",
    "    filename=getfiles_hdf5(path+timestamp+directory)\n",
    "    data=load_h5_to_dic(path+timestamp+directory+r'\\\\'+filename[0])\n",
    "    data_list.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb6c2faf-4061-4e2b-82d2-308b2211f355",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "Key_field = '0'\n",
    "ramsey_times=data_list[0][0][Key_field]['ramsey_times']\n",
    "data=data_list[0][0][Key_field]['click_array']\n",
    "ramsey_detuning=data_list[0][0][Key_field]['ramsey_detuning']\n",
    "\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(4,1,figsize=(5,15))\n",
    "sigmoid_length = 5\n",
    "x = 2*np.array(ramsey_times)\n",
    "############################### Fit #################################\n",
    "\n",
    "plot_guess=0\n",
    "threshold=85\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    \n",
    "def ramsey_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "i=0\n",
    "ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel(r'$2\\tau$ (ms)')\n",
    "ax[0].legend()\n",
    "\n",
    "guess = [0.5*ramsey_detuning*1e6, 1e3, -0.5,-1,0*np.pi]\n",
    "est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, p_data.mean(0))\n",
    "T_half_period = 0.5/est[0]\n",
    "T_min = 0.5/(est[0]+std[0])\n",
    "T_max = 0.5/(est[0]-std[0])\n",
    "T_err = T_half_period-T_min\n",
    "\n",
    "\n",
    "# T_pi = fine[np.argmin(data_fit[:int(len(fine)*T_half_period*2/max(fine))])]\n",
    "# T_pio2 = T_pi-T_half_period/2\n",
    "T_pi_list=(-est[3]+np.arange(-5,5)*np.pi)/(2*np.pi*est[0])\n",
    "T_pi_list_p=T_pi_list[T_pi_list>0]\n",
    "T_pi = T_pi_list_p[np.argmin(T_pi_list_p)]\n",
    "T_pio2 = T_pi_list_p[np.argmin(T_pi_list_p)]-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "\n",
    "bins=np.arange(np.min(np.concatenate(data)),np.max(np.concatenate(data)),5)\n",
    "h2 = ax[2].hist(np.concatenate(data)  , bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].vlines(threshold, 0, np.max(h2[0]))\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "ax[1].plot(fine,data_fit)\n",
    "if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel(r'$2\\tau$ (ms)')\n",
    "ax[1].legend()\n",
    "plt_label = f'f = {est[0]:.5f}$\\pm${std[0]:.5f} kHz, $\\delta $f = {(est[0] - ramsey_detuning*1e6):.4f} kHz \\n T$_2$ = {est[1]:.0f}$\\pm${std[1]:.0f} ms'\n",
    "ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "ax[1].set_ylim(0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a652e4c-1033-4992-a410-a3a3f720c4dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3\\\\raman_echo_alternating\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = [\n",
    "    '20231207175742_\\\\', \n",
    "    '20231207225733_\\\\'\n",
    "]\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    directory=os.listdir(path+timestamp)[0]\n",
    "    filename=getfiles_hdf5(path+timestamp+directory)\n",
    "    data=load_h5_to_dic(path+timestamp+directory+r'\\\\'+filename[0])\n",
    "    data_list.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88a215e7-9cdf-42a4-99f9-2cb25592137f",
   "metadata": {},
   "outputs": [],
   "source": [
    "Key_field = '0'\n",
    "ramsey_times=data_list[0][0][Key_field]['ramsey_times']\n",
    "ramsey_detuning=data_list[0][0][Key_field]['ramsey_detuning']\n",
    "x = 2*np.array(ramsey_times)\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.ylabel(\"P\")\n",
    "plt.xlabel(r'$2\\tau$ (s)')\n",
    "plt.ylim(0,1)\n",
    "\n",
    "data=data_list[0][0][Key_field]['click_array']\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "\n",
    "plt.errorbar(x*1e-3, p_data.mean(0), p_data.std(0)/np.sqrt(len(data)), fmt = \"o\", color='k') \n",
    "guess = [0.5*ramsey_detuning*1e6, 1e3, -0.5,-1,0*np.pi]\n",
    "est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, p_data.mean(0))\n",
    "plt.plot(fine*1e-3,data_fit, color='k', alpha=0.6, label=f'No Pulse | f = {est[0]*1e3:.2f}$\\pm${std[0]*1e3:.2f} s', lw=3)\n",
    "#plt_label = f'f = {est[0]:.5f}$\\pm${std[0]:.5f} kHz, $\\delta $f = {(est[0] - ramsey_detuning*1e6):.4f} kHz \\n T$_2$ = {est[1]:.0f}$\\pm${std[1]:.0f} ms'\n",
    "\n",
    "data=data_list[1][0][Key_field]['click_array']\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "\n",
    "plt.errorbar(x*1e-3, p_data.mean(0), p_data.std(0)/np.sqrt(len(data)), fmt = \"o\", color='indianred') \n",
    "guess = [0.5*ramsey_detuning*1e6, 1e3, -0.5,-1,0*np.pi]\n",
    "est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, p_data.mean(0))\n",
    "plt.plot(fine*1e-3,data_fit, color='indianred', alpha=0.6, label=f'Pulse | f = {est[0]*1e3:.2f}$\\pm${std[0]*1e3:.2f} s', lw=3)\n",
    "\n",
    "#plt.title(plt_label, fontsize = \"small\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e1053e9-468f-452d-99cc-2a7c3bd256b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data_list[0][0][Key_field]['click_array']\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "print(p_data.shape)\n",
    "chunk = 75\n",
    "plot_guess = 0\n",
    "plt.figure(figsize=[5,3*data.shape[0]//chunk])\n",
    "Ramsey_freqs0 = []\n",
    "Ramsey_freq_errors0 = []\n",
    "T2stars0 = []\n",
    "T2star_errors0 = []\n",
    "\n",
    "for i in range(data.shape[0]//chunk):\n",
    "\n",
    "    y = (data[i*chunk:(i+1)*chunk,:,0]>85).mean(0)\n",
    "    plt.subplot(data.shape[0]//chunk,1,i+1)\n",
    "    est,std,fine,data_fit = fit_function(guess, ramsey_fit, x,y)\n",
    "    T2stars0.append(est[1])\n",
    "    Ramsey_freqs0.append(est[0])\n",
    "    Ramsey_freq_errors0.append(std[0])\n",
    "    T2star_errors0.append(std[1])\n",
    "    plt_label = f'f = {est[0]*1e3:.5f}$\\pm${std[0]*1e3:.5f} Hz, $\\delta $f = {(est[0] - ramsey_detuning*1e6)*1e3:.4f} Hz \\n T$_2$ = {est[1]:.0f}$\\pm${std[1]:.0f} ms'\n",
    "    \n",
    "    plt.plot(x, y,\"o\")\n",
    "    \n",
    "    plt.title(plt_label, fontsize = \"small\")\n",
    "    if plot_guess: plt.plot(fine,ramsey_fit(fine, *guess))\n",
    "    plt.plot(fine,data_fit)\n",
    "    plt.ylabel(r\"$P_{~|\\downarrow\\downarrow\\rangle}$\")\n",
    "    plt.xlabel('Ramsey time (ms)')\n",
    "    \n",
    "    plt.ylim([0,1])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c28a5cb-05ff-485b-92ee-d449210d40a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data_list[1][0][Key_field]['click_array']\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "print(p_data.shape)\n",
    "chunk = 75\n",
    "plot_guess = 0\n",
    "plt.figure(figsize=[5,3*data.shape[0]//chunk])\n",
    "Ramsey_freqs1 = []\n",
    "Ramsey_freq_errors1 = []\n",
    "T2stars1 = []\n",
    "T2star_errors1 = []\n",
    "\n",
    "for i in range(data.shape[0]//chunk):\n",
    "\n",
    "    y = (data[i*chunk:(i+1)*chunk,:,0]>85).mean(0)\n",
    "    plt.subplot(data.shape[0]//chunk,1,i+1)\n",
    "    est,std,fine,data_fit = fit_function(guess, ramsey_fit, x,y)\n",
    "    T2stars1.append(est[1])\n",
    "    Ramsey_freqs1.append(est[0])\n",
    "    Ramsey_freq_errors1.append(std[0])\n",
    "    T2star_errors1.append(std[1])\n",
    "    plt_label = f'f = {est[0]*1e3:.5f}$\\pm${std[0]*1e3:.5f} Hz, $\\delta $f = {(est[0] - ramsey_detuning*1e6)*1e3:.4f} Hz \\n T$_2$ = {est[1]:.0f}$\\pm${std[1]:.0f} ms'\n",
    "    \n",
    "    plt.plot(x, y,\"o\")\n",
    "    \n",
    "    plt.title(plt_label, fontsize = \"small\")\n",
    "    if plot_guess: plt.plot(fine,ramsey_fit(fine, *guess))\n",
    "    plt.plot(fine,data_fit)\n",
    "    plt.ylabel(r\"$P_{~|\\downarrow\\downarrow\\rangle}$\")\n",
    "    plt.xlabel('Ramsey time (ms)')\n",
    "    \n",
    "    plt.ylim([0,1])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58cf1d53-4616-4ab6-94a6-c26b5d715d7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.errorbar(np.linspace(0,len(Ramsey_freqs0)-1,len(Ramsey_freqs0)),np.multiply(1e3,Ramsey_freqs0),yerr = np.multiply(1e3,Ramsey_freq_errors0), fmt = \"o\", capsize = 10)\n",
    "plt.errorbar(np.linspace(0,len(Ramsey_freqs1)-1,len(Ramsey_freqs1)),np.multiply(1e3,Ramsey_freqs1),yerr = np.multiply(1e3,Ramsey_freq_errors1), fmt = \"o\", capsize = 10)\n",
    "plt.ylabel(\"Ramsey frequency (Hz)\")\n",
    "plt.xlabel(\"Chunk no.\")\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.errorbar(np.linspace(0,len(T2stars0)-1,len(T2stars0)),np.multiply(1e3,T2stars0),yerr = np.multiply(1e3,T2star_errors0), fmt = \"o\", capsize = 10)\n",
    "plt.errorbar(np.linspace(0,len(T2stars1)-1,len(T2stars1)),np.multiply(1e3,T2stars1),yerr = np.multiply(1e3,T2star_errors1), fmt = \"o\", capsize = 10)\n",
    "plt.ylabel(r\"$T_2$ (Hz)\")\n",
    "plt.xlabel(\"Chunk no.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56ae1259-974a-4143-9c0b-f6038427cad1",
   "metadata": {},
   "source": [
    "# T1 measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a8e6e72-5320-4764-ad0a-e5f2f068eb90",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\nuclear_T1\\\\'\n",
    "timestamp_list =['\\\\'+direc+'\\\\' for direc in os.listdir(path)[-5:]]\n",
    "timestamp_list = [\n",
    "    #'20231218182930',  # Measure each second\n",
    "    '20231227154625',   # Measure each 10 seconds\n",
    "    #'20231231112340',   # Measure each 10 seconds + Purcell enhancement\n",
    "]\n",
    "\n",
    "data_list=[]\n",
    "for timestamp in timestamp_list:\n",
    "    filenames=getfiles_hdf5(path)\n",
    "    for filename in filenames:\n",
    "        if timestamp in filename: \n",
    "            data=load_h5_to_dic(path+r'\\\\'+filename)\n",
    "            data_list.append(data)\n",
    "    \n",
    "def lorentz(delta,delta0,kappa,a,b):\n",
    "    return a/(1+(delta-delta0)**2/(kappa/2)**2)+b\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fff211c8-2304-4156-88ab-1f5873872a63",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "plot_guess = 0\n",
    "data=data_list[0][0][Key_field]['click_array']\n",
    "time_data=data_list[0][0][Key_field]['time_data']\n",
    "\n",
    "\n",
    "measure_time = (time_data - time_data[0])*1e-9\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig, ax = plt.subplots(3,1,figsize=[10,10],tight_layout=True,sharex=True)\n",
    "ax[0].plot(measure_time[:,0]/60, data[:,0], '.', color=colors[1])\n",
    "ax[0].set_ylabel(f'{labels[1]}')\n",
    "ax[1].plot(measure_time[:,0]/60, data[:,1], '.', color=colors[2])\n",
    "ax[1].set_ylabel(f'{labels[2]}')\n",
    "ax[2].plot(measure_time[:,0]/60, data[:,2], '.', color=colors[3])\n",
    "ax[2].set_ylabel(f'{labels[3]}')\n",
    "ax[2].set_xlabel('Time (min)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8618e183-0985-46b1-9b3f-78fdb051bbc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rolling_average_and_std(arr, window_size):\n",
    "    \"\"\"\n",
    "    Calculate the rolling average and variance of a 1D array, preserving the original array size.\n",
    "    At the start and end of the array, the function uses partial windows.\n",
    "\n",
    "    :param arr: Input array\n",
    "    :param window_size: The size of the moving window\n",
    "    :return: Tuple of arrays (rolling averages, rolling variances)\n",
    "    \"\"\"\n",
    "    if window_size <= 0:\n",
    "        raise ValueError(\"Window size must be positive\")\n",
    "\n",
    "    # Pad the array with 'reflect' mode to handle edges while keeping the array size\n",
    "    padded_arr = np.pad(arr, (window_size//2, window_size - 1 - window_size//2), mode='reflect')\n",
    "    \n",
    "    # Initialize arrays to store the rolling averages and variances\n",
    "    rolling_avg = np.zeros_like(arr, dtype=float)\n",
    "    rolling_var = np.zeros_like(arr, dtype=float)\n",
    "    \n",
    "    # Calculate rolling averages and variances\n",
    "    for i in range(len(arr)):\n",
    "        start = max(0, i - window_size//2)\n",
    "        end = min(len(arr), i + window_size//2 + 1)\n",
    "        window_elements = padded_arr[start:end]\n",
    "        rolling_avg[i] = np.mean(window_elements)\n",
    "        rolling_var[i] = np.var(window_elements)**(1/2) \n",
    "\n",
    "    return rolling_avg, rolling_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9d32002-aecf-4347-bef8-9f0160ad17cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "minT, maxT = 0, 1e9\n",
    "mask = np.logical_and(measure_time>minT, measure_time<maxT)[:,0]\n",
    "\n",
    "masked_data = data[mask]\n",
    "masked_time = measure_time[mask]\n",
    "\n",
    "max_data = np.zeros(masked_data.shape)\n",
    "min_data = np.zeros(masked_data.shape)\n",
    "for i in range(masked_data.shape[0]):\n",
    "    jj = np.argmax(masked_data[i])\n",
    "    max_data[i, jj] = masked_data[i, jj]\n",
    "    \n",
    "    kk = np.argmin(masked_data[i])\n",
    "    min_data[i, kk] = masked_data[i, kk]\n",
    "\n",
    "\n",
    "threshold=75\n",
    "base_counts = masked_data.copy() \n",
    "base_counts *= (base_counts < threshold)\n",
    "base_counts[0] = [50,50,50]\n",
    "\n",
    "for i in range(3):\n",
    "    for j in range(base_counts.shape[0]-1):\n",
    "        if base_counts[j+1, i]==0: \n",
    "            base_counts[j+1, i] = base_counts[j, i]\n",
    "\n",
    "avgd_base_counts, std_base_counts = rolling_average_and_std(base_counts, 100)\n",
    "\n",
    "for i in range(3):\n",
    "    plt.figure(figsize=(10,3))\n",
    "    plt.plot(masked_time[:,0]/60, masked_data[:,i], '.', color=colors[i+1], label=labels[i+1])\n",
    "\n",
    "    plt.plot(masked_time[:,0]/60, base_counts, '.', color='k', alpha=0.01)\n",
    "    plt.plot(masked_time[:,0]/60, avgd_base_counts, '-', color='k')\n",
    "    plt.plot(masked_time[:,0]/60, avgd_base_counts+std_base_counts*2, '--', color='k')\n",
    "    plt.plot(masked_time[:,0]/60, avgd_base_counts-std_base_counts*2, '--', color='k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33a08c1b-ae18-438b-9e72-9f1d9e17646b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e5ebb32-206e-4079-9409-174d024fefcb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4e1b69b-cef1-4762-a976-b20b5f81eefb",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = masked_time[:,0]/60\n",
    "blue_trace = [100 if d<220 else 50 for d in masked_data.sum(1) ]\n",
    "all_data = np.array([blue_trace, *list(masked_data.T)]).T\n",
    "p0,p1,p2,p3, dx, dy = extract_populations_4state(all_data, frequency_domain = True, accumulated = True,averaged=False)\n",
    "pops = [p0,p1,p2,p3]\n",
    "\n",
    "state = p0*0 + p1*1 + p2*2 + p3*3\n",
    "\n",
    "threshold=75\n",
    "for i in range(len(state)-2):\n",
    "    if (state[i] != state[i+1]) and (state[i+1] != state[i+2]):\n",
    "        state[i+1] = state[i]\n",
    "    if all_data[i,state[i]] < threshold:\n",
    "        state[i] = 0\n",
    "\n",
    "for i in range(4):\n",
    "    pops[i] = state==i\n",
    "\n",
    "plt.figure(figsize=(10,3))\n",
    "for i in range(4):\n",
    "    plt.fill_between(x, pops[i]*140, color=colors[i], alpha=0.5,linewidth=0.0)\n",
    "\n",
    "for i in range(3):\n",
    "    plt.plot(x, masked_data[:,i], '.', color=colors[i+1], label=labels[i+1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b7a9076-155d-4417-995b-c6320a0a4cf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.special import erf\n",
    "\n",
    "def ErfRising(t, sigma, n_sigma, amplitude=1):\n",
    "    values = amplitude * (spec.erf((t - sigma / 2) / (sigma/n_sigma)) + 1) / 2\n",
    "    return values\n",
    "\n",
    "def generate_flattop_pulse(t, duration, sigma, n_sigma, amplitude=1):\n",
    "    \"\"\"\n",
    "    Generate a flattop pulse with an error function as the transient raise.\n",
    "\n",
    "    :param t: Array of time points at which the pulse is evaluated.\n",
    "    :param start: Start time of the pulse.\n",
    "    :param duration: Duration of the flat top of the pulse.\n",
    "    :param rise_time: Time over which the pulse rises and falls.\n",
    "    :return: Array of pulse values at the time points in t.\n",
    "    \"\"\"\n",
    "    \n",
    "    if t < sigma:\n",
    "        return ErfRising(t, sigma, n_sigma, amplitude=amplitude)\n",
    "    if t > sigma and t < sigma + duration:\n",
    "        return amplitude\n",
    "    if t > sigma+duration:\n",
    "        return amplitude-ErfRising(t-sigma-duration, sigma, n_sigma, amplitude=amplitude)\n",
    "    \n",
    "    return pulse\n",
    "\n",
    "# Time parameters for the pulse\n",
    "duration = 7\n",
    "sigma=6\n",
    "n_sigma = 6\n",
    "\n",
    "time_points = np.linspace(-100, 100, 10000)\n",
    "# Generate the pulse\n",
    "pulse = [generate_flattop_pulse(t, duration, sigma, n_sigma) for t in time_points]\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.plot(time_points, pulse, label='Flattop Pulse')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Amplitude')\n",
    "plt.title('Flattop Pulse with Error Function Transients')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82bf76fe-57ee-479c-8207-53b6b319cc22",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fft(x, y):\n",
    "    fft_x = np.fft.fftfreq(len(x),d=x[1]-x[0])\n",
    "    fft_y = np.abs(np.fft.fft(y))\n",
    "    return np.fft.fftshift(fft_x), np.fft.fftshift(fft_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8796dc01-1ac0-488b-ae89-3bd77fe8d715",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Plot 4 - FFT ###\n",
    "x=time_points\n",
    "y=np.array(pulse)\n",
    "fft_x, fft_y = fft(x, y)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(fft_x, fft_y/max(fft_y))\n",
    "plt.vlines(0.150, 0, 1)\n",
    "plt.xlim([-0.3, 0.3])\n",
    "plt.xlabel(\"Frequency (kHz)\")\n",
    "plt.ylabel(\"FFT\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acbf0ff6-dc55-4d72-a378-8dcd201f78e7",
   "metadata": {},
   "source": [
    "# DFS winter sale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ced1708-7dee-47dc-a761-143015d9d10f",
   "metadata": {},
   "outputs": [],
   "source": [
    "path='Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\DFS'\n",
    "timestamp_list = grab_timestamps_hdf(path, \"20240217132513\",\"20240217132513\")\n",
    "data_list = load_from_directory(path, timestamp_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5498ff3-19e0-4eb7-8b1a-d7053eac6e7f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "585939da-ba69-421f-9d26-7123e4bb9e86",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_guess = 0\n",
    "\n",
    "###########################################################\n",
    "Key_field = '0'\n",
    "ramsey_times      = data_list[0][0][Key_field]['ramsey_times']\n",
    "timing            = data_list[0][0][Key_field]['timing']\n",
    "delta_f           = data_list[0][0][Key_field]['delta_f_Hz']\n",
    "data_total        = data_list[0][0][Key_field]['click_array']\n",
    "differential_pops = data_list[0][0][Key_field]['differential_pops']\n",
    "ramsey_detuning   = data_list[0][0][Key_field]['ramsey_detuning']\n",
    "\n",
    "print('Data shape:', data_total.shape)\n",
    "differential_pops = []\n",
    "plt_labels = [\"Decoherence free\", \"Free decoherence\"]\n",
    "\n",
    "for j in range(2):\n",
    "    data = data_total[:,:,j] \n",
    "    ############################### Plotting ###############################\n",
    "    fig,ax=plt.subplots(2,2,figsize=(14,8))\n",
    "    fig.suptitle(plt_labels[j])\n",
    "    sigmoid_length = 5\n",
    "    x = np.array(ramsey_times)\n",
    "    ############################### Fit #################################\n",
    "\n",
    "    if len(readout_freqs)==4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        p_data=pops[2]-pops[1]\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        pops = []\n",
    "        p_data = (data[:,:,-1]>80)\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "\n",
    "    def ramsey_fit(t,f,T,a,b,phi):\n",
    "        return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "    ##############################################################\n",
    "    for i in range(len(readout_freqs)): ax[0,0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0,0].set_ylabel(\"Mean counts\")\n",
    "    ax[0,0].set_xlabel(r'$\\tau$ (ms)')\n",
    "    ax[0,0].legend()\n",
    "\n",
    "    p_data =(pops[2]+pops[1])-(pops[3]+pops[0])\n",
    "    differential_pops.append(p_data)\n",
    "    fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "    fft_y = np.abs(np.fft.rfft(p_data - p_data.mean()))\n",
    "    rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "    ##############################################################\n",
    "    if len(readout_freqs)==4:\n",
    "        ax[0,1].plot(x,p_data, 'o-', color='k', label = labels[2]+'+'+labels[1]+'-'+labels[3]+'+'+labels[0])\n",
    "    else:\n",
    "        ax[0,1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "\n",
    "    guess = [rabi_freq*1e-3, 3e3, -1, 0, 0.25*np.pi]\n",
    "    if plot_guess: ax[0,1].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "    try: est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, p_data)\n",
    "    except: est=guess; std=guess;fine=x;data_fit=p_data\n",
    "    \n",
    "    plt_label = f'f = {est[0]*1e3:.3f}$\\pm${std[0]*1e3:.3f} Hz, $\\delta $f = {(abs(est[0]) - ramsey_detuning*1e6)*1e3:.3f} Hz \\n T$_2^*$ = {est[1]*1e-3:.1f}$\\pm${std[1]*1e-3:.1f} s'\n",
    "    ax[0,1].plot(fine,data_fit)\n",
    "    ax[0,1].set_ylabel(\"P\")\n",
    "    ax[0,1].set_xlabel(r'$\\tau$ (ms)')\n",
    "    ax[0,1].legend()\n",
    "    ax[0,1].set_title(plt_label, fontsize = \"small\")\n",
    "    ax[0,1].set_ylim(-1,1)\n",
    "    \n",
    "    ##############################################################    \n",
    "\n",
    "    bins=np.arange(np.min(np.concatenate(data)),np.max(np.concatenate(data)),5)\n",
    "    for i in range(4):\n",
    "        h2 = ax[1,0].hist(np.concatenate(data[:,:,i])  , bins=bins   , label = labels[i], alpha=0.5)\n",
    "    ax[1,0].legend()\n",
    "    ax[1,0].set_ylabel(\"instances\")\n",
    "    ax[1,0].set_xlabel('counts')\n",
    "\n",
    "    ##############################################################\n",
    "    ax[1,1].plot(fft_x, fft_y)\n",
    "    ax[1,1].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "    ax[1,1].set_xlabel(\"Frequency (Hz)\")\n",
    "    ax[1,1].set_ylabel(\"FFT\")\n",
    "    ax[1,1].set_title(f\"Max frequency = {rabi_freq:.2f} Hz\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # save_fig_manustyle(directory+filename+'_'+'nuclear_spin_%i.pdf'%j)\n",
    "\n",
    "plt.figure(figsize = (12,5))\n",
    "plt.plot(1e-3*delta_f)\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"$\\Delta_{f}$ (kHz)\")\n",
    "plt.tight_layout()\n",
    "# save_fig_manustyle(directory+filename+'_'+'frequency_drift.pdf')\n",
    "plt.show()    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50d7748e-138d-447a-b2b7-78e849bf9ab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt_labels = [\"Decoherence free\", \"Free decoherence\"]\n",
    "\n",
    "for j in range(2):\n",
    "    data = data_total[:,:,j]\n",
    "    ############################### Plotting ###############################\n",
    "    fig,ax=plt.subplots(2,2,figsize=(12,7), tight_layout=True, sharex = True)\n",
    "    plt.subplots_adjust(hspace=0, bottom = 0, top = 0.01)\n",
    "    fig.suptitle(plt_labels[j])\n",
    "    ax = ax.flatten()\n",
    "    ############################### Fit #################################\n",
    "\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    for i in range(4): \n",
    "        ax[i].plot(x,pops[i], 'o-', label = labels[i], color=colors[i])\n",
    "        ax[i].set_ylabel(\"P\")\n",
    "        ax[i].legend()\n",
    "        ax[i].set_ylim(0, 0.5)\n",
    "    for i in [-1,-2]: ax[i].set_xlabel(r'$\\tau$ (ms)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c36c1e9-6cec-493c-a3cc-b2872812d695",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "\n",
    "x = np.array(ramsey_times)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "plt_labels = [\"Decoherence free\", \"Free decoherence\"]\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "# Low pass filtering\n",
    "sampling_frequency_Hz = 1e3/(x[1]-x[0])\n",
    "f_cut = 4.95 # in Hz\n",
    "\n",
    "for i,differential_pop in enumerate(differential_pops):\n",
    "    \n",
    "    # Optional low pass filter\n",
    "    # differential_pop = filter_signal_highlow(differential_pop, sampling_frequency_Hz, f_cut, filter_type = 'lp', order = 4)\n",
    "    \n",
    "    fft_y = np.abs(np.fft.rfft(p_data - p_data.mean()))\n",
    "    rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    \n",
    "    guess = [rabi_freq*1e-3, 1e3, -1, 0, 0.25*np.pi]\n",
    "\n",
    "    est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, differential_pop)\n",
    "\n",
    "    plt.plot(fine,data_fit,color = colors[i])\n",
    "    plt.plot(x,differential_pop,\"o\", color = colors[i], label = plt_labels[i]+f\", T$_2^*$ = {est[1]*1e-3:.2g}$\\pm${std[1]*1e-3:.2g} s\")\n",
    "\n",
    "plt.xlabel(r'$\\tau$ (ms)')\n",
    "plt.ylabel(r\"$P_{|\\uparrow\\downarrow\\rangle}+P_{|\\downarrow\\uparrow\\rangle}-P_{|\\uparrow\\uparrow\\rangle}-P_{|\\downarrow\\downarrow\\rangle}$\")\n",
    "plt.legend(fontsize = \"small\")\n",
    "plt.tight_layout()\n",
    "# plt.savefig(directory+filename+'_'+'decoherence_comparison.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43ece8b6-aec5-4568-8e32-c556dc4d3a22",
   "metadata": {},
   "outputs": [],
   "source": [
    "chunk_size = 40\n",
    "offset = 0\n",
    "npts = data_total.shape[0]-data_total.shape[0]%20\n",
    "\n",
    "for start in offset+np.linspace(0,npts-chunk_size,npts//chunk_size,dtype = int):\n",
    "    stop = start+chunk_size\n",
    "    print(start,stop)\n",
    "    plt.figure(figsize=(14,5))\n",
    "    for j in range(2):\n",
    "        data = data_total[start:stop,:,j] \n",
    "        ############################### Plotting ###############################\n",
    "        plt.subplot(1,2,j+1)\n",
    "        sigmoid_length = 5\n",
    "        x = np.array(ramsey_times)\n",
    "        ############################### Fit #################################\n",
    "    \n",
    "        if len(readout_freqs)==4:\n",
    "            p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "            pops = [p0,p1,p2,p3]\n",
    "            p_data=pops[2]-pops[1]\n",
    "            labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "        else:\n",
    "            pops = []\n",
    "            p_data = (data[:,:,-1]>80)\n",
    "            labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "    \n",
    "        def ramsey_fit(t,f,T,a,b,phi):\n",
    "            return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "    \n",
    "        ##############################################################\n",
    "    \n",
    "        p_data =(pops[2]+pops[1])-(pops[3]+pops[0])\n",
    "        fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "        fft_y = np.abs(np.fft.rfft(p_data - p_data.mean()))\n",
    "        rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    \n",
    "        ##############################################################\n",
    "        if len(readout_freqs)==4:\n",
    "            plt.plot(x,p_data, 'o', color='k', alpha = 0.75, label = labels[2]+'+'+labels[1]+'-'+labels[3]+'+'+labels[0])\n",
    "        else:\n",
    "            plt.errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "    \n",
    "        guess = [rabi_freq*1e-3, 3e3, -1, 0, 0.25*np.pi]\n",
    "        if plot_guess: plt.plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "        try: est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, p_data)\n",
    "        except: est=guess; std=guess;fine=x;data_fit=p_data\n",
    "        \n",
    "        plt_label = f'f = {est[0]*1e3:.3f}$\\pm${std[0]*1e3:.3f} Hz, $\\delta $f = {(abs(est[0]) - ramsey_detuning*1e6)*1e3:.3f} Hz, T$_2^*$ = {est[1]*1e-3:.1f}$\\pm${std[1]*1e-3:.1f} s'\n",
    "        plt.plot(fine,data_fit)\n",
    "        plt.ylabel(\"P\")\n",
    "        plt.xlabel(r'$\\tau$ (ms)')\n",
    "        plt.legend()\n",
    "        plt.title(plt_labels[j]+\"\\n\"+plt_label, fontsize = \"small\")\n",
    "        plt.ylim(-1,1)\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "682b2e65-98ea-4b95-a6d0-aac6704c0198",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Testing filter functions\n",
    "\n",
    "############### Define a test signal ###############\n",
    "testfreq = 10/sampling_frequency_Hz\n",
    "time_axis = np.linspace(0,10000,10001)\n",
    "photon = np.zeros_like(time_axis)\n",
    "start,stop = 1000,3000\n",
    "photon[start:stop] = gauss(1, 0, 200, stop-start) *np.sin(testfreq*2*np.pi*time_axis[start:stop])\n",
    "start,stop = 5000,6000\n",
    "photon[start:stop] = gauss(1, 0, 200, stop-start)*np.sin(testfreq*4*np.pi*time_axis[start:stop])\n",
    "\n",
    "\n",
    "############### filter it ###############\n",
    "stopband_l,stopband_h = 15,40 # in units of sampling frequency\n",
    "sampling_frequency_Hz = 1000\n",
    "\n",
    "plt.plot(time_axis,photon, alpha = 0.5)\n",
    "plt.plot(time_axis,filter_signal_band   (photon,sampling_frequency_Hz,f_low = stopband_l,f_high = stopband_h, filter_type = 'bs'), alpha = 0.5)\n",
    "plt.plot(time_axis,filter_signal_highlow(photon,sampling_frequency_Hz,f_cut = 15, filter_type = 'hp'), alpha = 0.5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13aab0ce-4547-4f4f-b7c4-d22c35456457",
   "metadata": {},
   "source": [
    "# Tomography"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "f86892ed-e232-4813-b965-1b0f528347d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-29T07:52:04.709976Z",
     "iopub.status.busy": "2024-03-29T07:52:04.708975Z",
     "iopub.status.idle": "2024-03-29T07:52:04.910506Z",
     "shell.execute_reply": "2024-03-29T07:52:04.909506Z",
     "shell.execute_reply.started": "2024-03-29T07:52:04.709976Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading 20240328184732_Tomography.hdf5                                        \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mSignature:\u001b[0m \u001b[0mplot_tomo_results\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpops\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrho\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_state\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mDocstring:\u001b[0m\n",
       "arguments are the 4 populations, best fit density matrix and target state\n",
       "plots matices of populations for each state, heatmaps of Re(rho) and Im(rho) and a city plot of Re(rho)\n",
       "target_state can be any of the following:\n",
       "\"00+11\"\n",
       "\"10+11\"\n",
       "\"01+11\"\n",
       "\"00+10+01+11\"\n",
       "\"01\"\n",
       "\"10\"\n",
       "\"00\"\n",
       "\"11\"\n",
       "\"01+10\"\n",
       "\u001b[1;31mFile:\u001b[0m      c:\\users\\manipp102\\appdata\\local\\temp\\ipykernel_21708\\925749312.py\n",
       "\u001b[1;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 1 files                                                                \r"
     ]
    }
   ],
   "source": [
    "path='Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\Tomography'\n",
    "\n",
    "timestamp_list, target_state = [\"20240328184732\"], \"01\"\n",
    "\n",
    "data_list = load_from_directory(path, timestamp_list)\n",
    "plot_tomo_results?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "d462c352-43ea-48c7-aeca-824e971ab780",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-29T07:52:04.975507Z",
     "iopub.status.busy": "2024-03-29T07:52:04.975507Z",
     "iopub.status.idle": "2024-03-29T07:52:08.636555Z",
     "shell.execute_reply": "2024-03-29T07:52:08.635559Z",
     "shell.execute_reply.started": "2024-03-29T07:52:04.975507Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data shape: (1300, 3, 3, 4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "()"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Key_field = '0'\n",
    "data = data_list[0][0][Key_field]['click_array'][:]\n",
    "print(\"data shape: %s\"%(str(data.shape)))\n",
    "pops, rho, fidelity = twoqb_tomo(data,target_state)\n",
    "plot_tomo_results(pops, rho, target_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "1d18e55a-0887-4e34-9d05-55cf0a3fd090",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-29T07:52:16.839387Z",
     "iopub.status.busy": "2024-03-29T07:52:16.838387Z",
     "iopub.status.idle": "2024-03-29T07:52:29.326442Z",
     "shell.execute_reply": "2024-03-29T07:52:29.325442Z",
     "shell.execute_reply.started": "2024-03-29T07:52:16.839387Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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66KPMnDnTyldEioJx63OR4lCY95vuUUQKT48FxGqM65DzonXvIiIiRWvjxo1cuHABX19fU9AIsnainTp1Kk5OTqxZs4akpKR82/r+++8B6NOnjyloBGAwGBg1ahS+vr6EhIQQHBxsKlu7di0AEyZMMAWNAOrWrcvgwYMB2LlzZ7Z+IiIiAGjdunXBXqyISD5K4h4lPSOD0JjLbDl6ltCYyzkSb+dXLlLaaMaRWJXWvYuIiJSsoKAgALp27ZqjrHr16nTo0IGgoCB2795Nz54982zLuJnIc889Z7b8iSeeYM+ePQQHB9O5c2cA1q1bx5kzZ2jRokWO+hn/f3NUuXLlbMePHz8OKHAkUpzSMzIqzHLD4rxHCY44Z6YfR/y7++DnWS/fcpHSSIEjsTqtexcRESk5kZGRADRv3txsedOmTQkKCuLUqVP5Bo7S09MBcs2HVKlS1k/J6Oho0zFHR0datmyZo+7hw4f59ttvsbGx4cUXX8xWZgwcJScn8/rrr3Ps2DFu3LhB48aNefnllxkwYAA2+h0hYjUVMXhRHPcowRHnzO4CF5d0m/EBexj0ZAtW/vdkruXT+vkW6PpXpOCflCwFjqRIaN27iIhIybh8+TIANWua/x52c8vK4xEXF5dvW40bNyY6OpoDBw7w+OOP5yg/dOgQAAkJCWbPv3XrFuPHjycmJobIyEiqVavGzJkz8fH5Y9mIMTE2wKeffkrDhg1p27YtFy5c4OTJk3zyySfs2bOHL774Altb23zHLFKalYYb/fyCG8bgRWkYq7UV5T1KekYGczaF5lnn+72n8iyfs+kwT7eoa9F1rojBPyk5ChyJiIiIlCO3b2fdRDg4mN8hyHg8OTk537b69u3L1q1bWbRoEV5eXnTq1MlUFhAQwLZt2wC4c+eO2fPPnz/P5s2bTX82GAxERkbSrVs302ylc+fOcePGDWxtbZkyZQp9+vQx1Q8LC+PNN99k27ZtLFmyhDFjxpjtJzY2NtfldADbt2/P97WKFDVLb/SLMmBjSXBjzqbDZGRmMnfzYQUlCiDsbHyeCbgBMjIz8yyPS0om7Gx8vsEtS4N/Inl9N8bGxlK7dm2L2lHgSERERKQcsbW1NeUSyktmPjcwAJ07d2bIkCEsW7aM0aNH07JlSzw8PIiOjiY6OprBgwezYsUKUxDofh4eHuzfvx9bW1sOHjzI1KlTWbBgARcuXDDtnla/fn327t1LUlISDRs2zHa+t7c3H330EWPHjjWNwbh7m0hZYumNflHPIrEkuBGXlMyE1f/Nd6yS3ZWbKcXSjqXBP0tnLolYQoEjERERkXKkSpUqJCYmkpqaarY8JSXrpsTJycmi9iZMmIC3tzfLly/n5MmTnD9/Hm9vbyZNmoSDgwMrVqzAxcUl17EYPfvsszRu3Jg///nPrF+/njFjxtC0aVMAXF1dcXV1NduGn58ftra2XLlyhdjYWOrUqZOjTu3atTWrSEpcbjOFCjLLp6gDNtYIbigoYV4NZ/OzPK3djqXBP0tmLkn5l9d3Y16zke6nwJE8kPK45llERKQ8cHd3JzExkfj4eLNT0I25jdzd3S1us1evXvTq1SvH8cDAQCBrZpElGjRoQNu2bdm7dy/Hjx83BY7yYmdnh6urK/Hx8aZleCKlTV4zhVwc7Sy60f9s46E861gjYGON4EZBgxIV5b7Bu4Eb7i6Oef5d2xgMeS5Xc3dxwruBW579WBr8s9YMKBFQ4EgegBKxiYiIlF7NmzcnMjKS3377jTZt2uQoP336tKlefi5dukR0dDT16tWjXr2c3/F79+4FMPWTkJDAV199RXJyMtOmTTPbpp2dHQBpaWkAbNu2jU2bNtG6dWuGDRuWo35qaiqJiYnY2NjkmvBbpCTltwyt/xPNLGonMdn8LME/2iv8LBJLghuWsDQoUZHuG2xtbPDv7mP2vWD0asfmZndVM/Lv3jbfoJqlwT9rzYASASh/oV4pUsYvxvu/bIxfjMER50poZCIiIgJZS7sAtmzZkqPs2rVr7N+/H3t7ezp27JhvW7t27WL48OEsXLgwR1lcXBxbt26lcuXKdO3aFchKvB0QEMCPP/7I0aNHc5yTmJhIWFgYAF5eXgDcuHGD9evXs2zZMlMw6V4bNmwgLS0NHx8fnJ2d8x2zSHGyZBna5vCzVuuvsLNIjMGNwrIkKFER7xv8POsxrZ8v7i6O2Y67uzgxrZ8vb3Z9NM9yS4JpxuBfXiyZuSRSEAocicUsXZ+dbkFCThERESkaXbp0oW7dugQHB7Nq1SrT8ZSUFCZMmEBycjL9+vXLllMoLS2NqKgooqKisgVvnnnmGezt7Vm3bh3h4eGm49evX8ff35+UlBT69+9vyjtUpUoVXnzxRSArN1J8fLzpnKtXr/K3v/2NxMREnnnmGVq0aAHAn/70J2rUqMGFCxeYNm1atv5DQkKYNm0aBoOBN99807oXSsQKLMk3k5icSjUn+zzr5FduZI1ZJHkFN/7x8pNWCUpU5PsGP896/DiuN/OHduaTvh2ZP7QzP47rZQoK5VeeH0uCf5bMXBIpCC1VE4spEZuIiEjp5+DgwIwZMxg5ciSTJk0iICAADw8PDh8+TFxcHK1bt8bf3z/bOZcvX6Znz55AViJNY86imjVrMn78eCZPnsyrr77K448/jr29PQcPHuTmzZs89dRTvPvuu9naev/99zlx4gRHjx6la9eu+Pj4kJmZSXh4ODdu3KBVq1b885//NNV3dnZm9uzZvP7666xcuZKgoCA8PT25evUqhw8fBuCDDz6waIaUSHGzdAZQtzYN+GFfZK7l7/Z8jLlbDuf5W9uas0j8POvxdIu6ZnMP2dgY8lxuZUlQoqLfN9ja2OT5uvIrz48x+JdzGaAT/t3blrtlgFLyFDgSiykRm4iISNnQvn17Vq9ezbx58zhw4ACnT5/Gw8ODfv36MXz48Gy7neXn1VdfxdXVlW+++YbQ0FDs7Oxo3LgxL7/8Mi+++KIpZ5GRs7Mz3333HUuXLmXDhg0cPHgQGxsbHnnkEXr16sXgwYNznNOhQwfWrl3LwoUL2bNnD7/++ivOzs507tyZESNG0K5dO6tcFxFrs3QG0DPN6/Jofbc8b/StEbApiNyCF9YISpTV+4aylMg7r+CfiLUpcCQWUyI2ERGRsqNZs2Z88cUXFtX18PDg1KlTuZZ369aNbt26Wdy3nZ0do0ePZvTo0Raf06BBA6ZOnWpxfSn9ytJN+IOyJNm0caaQrY1Nnjf6pWkWSWGDEmXxvsHSRN6l6X1d2JlLIpZS4EgsVpAvRhERERGpuCrKblqW7KR170yh/G70S9MsksIEJcrafUN+O+MZE1dXlPe1yP3KV8hfipQSsYmIiIhIfirablr57aRV0ICCMWDT1asBPo1qlsnf1mXpvsHSRN47jv9eod7XIvfSjCMpkNI0hVZEREREShdLb8KfblG3VAQNrKU0zRQqLcrKfYOlibw/23gozzrl8X0tYqTAkRSYvhhFRERExJyKvJuW8s3kVBbuGyxN0J2YnJpneXl9X1dUpSmXVWmgwJE8EH0xioiIiMj9yupuWlJ0Svt9gzUTdOt9XT4ol1VOFTdkJiIiIiIiVlUWd9OSis2YyDsv1ZzsLWpL7+uyr6LlaLOUAkciIiIiImIVltyEl6bdtEQsSeT9bs/H9L6uACzN0ZaekVFMIyo9FDgSERERERGrKEu7aZVn6RkZhMZcZsvRs4TGXK6QN7oFkd/OeM+2rq/3dQVQkBxtFY1yHImIiIiIiNWUld20yivlZ3kw+SXy1vu6/FOOttwpcCQiIiIiIlZVFnbTKo+M+VnuZ8zPMq2frwIcecgvkXdxvq+1q1fRye3aKkdb7hQ4EhERERERqyvtu2mVN5bmZ3m6RV0FIAqhON7XmjVWdPK6tk+3qIu7i2Oey9Uqai4rfWKIiIiIiIiUccrPUj5oV6+ik9+13XXygnJZ5aLivWIREREREZFyRvlZyj7t6lV0CjIjL69E6RV1xpeWqomIiIiIiJRxJZGfRXl4rKsgs8a0DLRgCnJtlaMtJwWOREREREREyjjvBm7Fmp9FeXisT7PGik5Br61ytGVXcUNmIiIiIiIi5YStjU2x5WdRHp6ioV29io6ubeEocCQiIiIiIibpGRmExlxmy9GzhMZcVj6V/1cWroufZ70iz8+iPDxFxzhrLC8VdVevwtK1LZxys1QtJiaG+fPnc+jQIRISEqhVqxY9evRg9OjRVKlSpUBt/fLLL6xcuZKIiAgyMjKoX78+PXv2ZNiwYTg65nyzffHFF8yfPz/X9vz8/Fi4cGG2YykpKSxbtoz169dz7tw5HB0dad++PX/5y19o2bJlgcYrIiIiImINWn5kXlm6LkWdn0V5eIqOcdbY+IA9udapqLt6FZaubeGUi8BReHg4Q4cOJTk5GW9vb7y8vAgNDWXBggXs2LGD7777joceesiitubOnctXX32Fra0tjz32GM7OzoSHh/P555+zYcMGVq5cSbVq1bKdc/z4cQA6d+6Ms7NzjjY9PT2z/TklJYWRI0dy8OBB3N3deeaZZ4iNjWXz5s3s2LGDf/3rXzz99NMPdjFERERERB6AcfnR/YzLjyrqjkJl8boUZX4W5eEpWsZZYzkDlU74d29b6t5rpU1eCdt1bR9cmQ8cpaWlMW7cOJKTk5k+fTovvfQSkBWc8ff3Z8eOHcyaNYvJkyfn21ZISAhfffUVLi4uLF++nBYtWgCQnJzMW2+9xa5du5g7dy6TJk3Kdt7x48extbVlzpw5Zmck3e9f//oXBw8e5JlnnuHLL7/EwSFrHeW6det4//33ef/999myZYvZIJSIiIiIiLUVZKvqivREvjRel5LeyUy5YoqedvV6MJbMDLTWtS3pf4fFrcwHjjZu3MiFCxfw9fU1BY0AHBwcmDp1Ks8++yxr1qzh7bffxsXFJc+2fvrpJwBGjBhhChoBODk5mQJHO3fuzHZOXFwc8fHxNGvWzKKg0a1bt1i+fDm2trZ8+umnpqARwAsvvMCvv/7Kxo0bWbduHYMGDbLoGoiIiIiIFIaWH5lX2q5LaVgyV9y7t1VU2tWrYAoyM7Cw17Y0/DssbmU+JBYUFARA165dc5RVr16dDh06kJaWxu7du/Nt65NPPuE///kPr776ao6y9PR0ACpVyh5rMy5Ta926tUXjDQkJ4datW3h5eVG7du0c5d27dwf+eF0iIiIiIkVNy4/MK03XpbTsZFacu7dJ6VDaE8MXZ8L20vLvsLiV+X/NkZGRADRv3txsedOmTQE4depUvm1VqlSJRx55hKpVq2Y7funSJWbMmAFA3759s5UZA0cuLi589NFH/OlPf8LLy4s//elPzJw5kxs3bmSrbxxHbuNt0qSJxeMVEREREbEGLT8yr7Rcl9K2k1lx7N4m1lHYoE9wxDn6fL6eN74NYlLgXt74Nog+n68vVQGSgswMLIzS9u+wOJX5pWqXL18GoGZN81PN3NyypkjGxcUVuO1//vOfHDlyhCNHjmAwGBg5ciSjRo3KVscYOFq6dCmurq60bduWWrVqcezYMRYvXszWrVtZvnw57u7u2cZh/PP9jMevXLlS4PGKiIiIiDwILT8yr7Rcl9K2ZA6Uh6csKOySqtKYGN5cbqHimhlYGv8dFpcyHzi6fTvrL+7eXEH3Mh5PTk4ucNuBgYEkJiYCYGdnR1xcHAkJCdSoUcNUJyIiAoBXX32VDz/8EDs7OyAroPX2228TEhLC+PHj+fe//51tHLnlQ7K3twcgIyOD27dvm60XGxvLc889l+u4t2/fXsBXKiIiUj7k9f0YGxtrdpm4iGir6tyUlutSmpbM3Ut5eEqvwgZ9SmNi+NwCYS/4NLbo/MLODCyt/w6LQ5n/5Le1tbWoXmZmZoHbXrt2LWFhYaxevRofHx9+/vlnBg0aRErKH2+EjRs38vPPPzNp0iRT0AiyZkDNnDkTR0dHdu/eTVRUVIHGC1nBIxERERGR4qDlR+aVhutSWpbMSdlgjSVVBZldUxw5kPLKLbQ4+BhVHe1yOTOLNWYGVuR/h2V+xlGVKlVITEwkNTXVbLkxyOPk5FTgto1PJdu0acPixYvp27cvkZGRBAYGmnY8c3Z2zjVfUe3atfH09OTQoUMcPXqUxo0bU6VKlWzjup/xddjY2OQ6K6l27dqaVSQiImJGXt+Pec1GEpEsWn5kXklfl9KyZE7KBmssqbJ01szOUxf45Kd9RbrDmCWBsPymiVhjZmBF/ndY5r8BjDmB4uPNJ7rKL6eQpezs7OjRowcAx44ds/g8Y/DJuETNmIspt/EaczY9/PDD2FTwL2gRERERKX7G5UddvRrg06hmhQ8aGZXkddFOZlIQ1lhSZemsmR/2RRb5DmOWBMKSbt9hlF/rIp0ZWJH/HZb5GUfNmzcnMjKS3377jTZt2uQoP336tKlefubOnUtUVBQffvghtWrVylFuXIp29+5dU9v//ve/sbGx4R//+IfZNmNjY4E/AkjGcRjHVZjxioiIiIhIxWBcMpczx4sT/t3bVtilhJKTNZZUWTK7xsZgICOPlDDWyoFkaSDM4+GH+HFc7yKdGVhR/x2W+cCRn58f69evZ8uWLfTt2zdb2bVr19i/fz/29vZ07Ngx37b27dtHaGgo3t7ejBgxIkf5r7/+CoCXlxeQlXj7xx9/BGDUqFE0bNgwW/0zZ85w5MgRnJycaN++PQCPPfYYzs7OHDlyhMuXL+fYDW7Tpk0AdO7c2YJXLyIiIiLyB3M7DpXHp98VVUkvmZOywRpLqixJDJ9X0Aist8NYQQJhxZGwvSL+Oyzzr6xLly7UrVuX4OBgVq1aZTqekpLChAkTSE5Opl+/fri6uprK0tLSiIqKIioqirS0NNPxgQMHAjBv3jzCw8Oz1Z85cyYHDhzAzc2NPn36AODh4UGnTp0A+OCDD7h69arpnEuXLvHWW2+Rnp7O8OHDcXZ2BrJ2TRswYABpaWmMHz+eW7dumc75+eef2bRpEw8//DAvv/yyNS+TiIiIiJRzwRHn6PP5et74NohJgXt549sg+ny+3mrLRaR00FJCyY+1llTllRi+/xPNLBpLQXYYyy3JtjEQlpfizi1U0f4dGjIfZLuxUubgwYOMHDmSlJQUWrVqhYeHB4cPHyYuLo7WrVuzbNkyU1JqgPPnz5sSZG7fvh0PDw9T2ccff8wPP/yAjY0Nbdu2xcXFhRMnTnDp0iVcXV1ZuHBhtiVxcXFxvPbaa5w5c4aHHnqItm3bAnDgwAFSUlLo1q0bs2fPplKlPyZ33b59m9dee42jR4/y8MMP065dOy5dukRYWBj29vYsXryYDh06mH2t945bRERELKfv0PJJf69Zctt626gi74omUlGZ376+4EuqzM1kDDsbzxvfBuV77vyhnS2aAWR+rH8k2dZnnPUV5PuzzC9VA2jfvj2rV69m3rx5HDhwgNOnT+Ph4UG/fv0YPnx4tqBRfj799FM6duzId999x/Hjx7lz5w516tRh6NChjBw5MkeSbXd3dwIDA1myZAlbtmxh3759VK5cGU9PT1555RVeeuklDAZDtnMcHR1ZtmwZixcv5pdffiEoKIjq1avTrVs3/vrXv9KiRQurXBcRERERKf8s3XrbGrlGRKTssNaSKnPLv6y5w1huQSFjkm1jUKgi5hYqLcrFjKOKpDieqmltvIiIlEeamVI+6e8VQmMuW/XJv4iIJawxCyg9I4M+n6/PNwD147he2NrY6F7ViircjCOxnvymCIqIiIhI6WKNrbdFRArKGrOAws7G5xk0guxJtosj+bXkpMCRmFg6RVBERERESg9rbL0tIvIgCrscToHvssFqgaOYmBiOHDlCXFwcV69eJTU1lWrVquHq6krjxo3x8fHB0THvTOhScrQ2XkRERKRssmauERGRgirMLCAFvsuGQgWODh8+TEBAALt27SIhISHPura2trRq1YrevXvzwgsv8NBDDxWma7Gygk4RFBEREZHSwbj1dl65RizZeltEpLgp8F02PFDgaN26dSxZsoTTp09zb25tJycnqlWrRrVq1bC3t+f69etcv36da9eucffuXcLCwggPD2fWrFk8//zzvPHGG9SuXdtqL0YenKYIioiIiJRd2nFIRMoiBb7LhgIFjvbv38+MGTM4ceIEmZmZVK1alW7dutGuXTu8vb1p0KCB2fNu3brFsWPHCAsLY8eOHRw5coQ1a9awfv16hgwZwpgxY3B2drbKC5IHoymCIiIiIiXHGjsFWWvrbRGR4qTAd+lXoMDR0KFDAXjqqacYMGAAnTp1onLlyvmeV6VKFTp06ECHDh0YPXo0586dY926daxYsYIlS5bg4ODAG2+88WCvQKxCUwRFRERESoY1d7XVjkMiUhYp8F26FShw9NRTTzF27Fi8vb0L1Wm9evV48803GTFiBCtWrMDJyalQ7UnhaYqgiIiISPHTrrYiIlnKW+DbGjNJS4sCBY6WLFli1c4dHR0ZNWqUVduUB6cpgiIiIuVDTEwM8+fP59ChQyQkJFCrVi169OjB6NGjqVKlSoHa2rt3L19//TVhYWHcuXOHevXq0bt3bwYOHGg21cCdO3dYunQp69ev58yZMzg4ONC8eXNeeeUVXnjhBbN93L17l8DAQH744QfOnDlDpUqV8PLyYtSoUTzxxBMPdA3KAu1qKyIlrTwFN0oTa84kLQ0KtaualD+aIigiIlK2hYeHM3ToUJKTk/H29sbLy4vQ0FAWLFjAjh07+O677yze3XbRokXMmjULgIYNG9K4cWOioqKYNWsWP//8M4sWLaJOnTqm+mlpaYwaNYp9+/bx0EMP0bFjR+7evcuhQ4c4ePAgO3fuZObMmRgMBtM5GRkZvPfee2zcuJGqVavy5JNPkpiYyH//+1/27NnD3//+d1555RXrXqRSQrvaikhJKm/BjdKiPM4kVeBIcihvUwRFREQqirS0NMaNG0dycjLTp0/npZdeAiAlJQV/f3927NjBrFmzmDx5cr5thYSEMGvWLAwGAx999BGDBg0CIDMzkyVLljBz5kzeffddVqxYYQoELV68mH379tG6dWuWLFlC9erVAbhw4QIDBw5kw4YNdOnShR49epj6CQwMZOPGjbRq1YpvvvmGqlWrAlkzncaMGcPf//53fH19swWoygvtaisiJaW4gxsVZWZTeZ1JWnZGKiIiIiJ52rhxIxcuXMDX19cUNAJwcHBg6tSpODk5sWbNGpKSkvJt6/vvvwegT58+pqARgMFgYNSoUfj6+hISEkJwcLCpbO3atQBMmDDBFDQCqFu3LoMHDwZg586d2fpZuHAhABMnTjQFjQA6duzI0KFDSU1NZcWKFRZegbJFu9qKSEmwNLiRnpFhlf6CI87R5/P1vPFtEJMC9/LGt0H0+Xw9wRHnrNJ+aVKQmaRlSZHPOBo/fnye5QaDAQcHB2rVqoWvry+tWrUq6iGJiIiIlEtBQUEAdO3aNUdZ9erV6dChA0FBQezevZuePXvm2dapU6cAeO6558yWP/HEE+zZs4fg4GA6d+4MwLp16zhz5gwtWrTIUT/j/29A7t2R9/Tp05w7dw43Nzd8fHxynNO9e3cWLVpEUFAQ7733Xp7jLYu0q62IlITiXCZbHpdt5aW8ziQt8sDRTz/9hMFgIDMz02z5vWVz5syhe/fufPbZZ1SqpFV0IiIiIgURGRkJQPPmzc2WN23alKCgIE6dOpVv4Cg9PR0g13xIxt9q0dHRpmOOjo60bNkyR93Dhw/z7bffYmNjw4svvmjxeJs0aYLBYODs2bOkpqZib2+f55jLGu1qKyIlobiCG+V12VZeyutM0iL/29m9ezctW7bk+eefZ/Xq1YSEhBASEkJAQADPP/88rVq1YuvWrQQGBtKzZ082bdrEokWLinpYIiIiIuXO5cuXAahZ0/wTYje3rJkrcXFx+bbVuHFjAA4cOGC2/NChQwAkJCSYLb916xZvvfUWvXv3ZsCAAaSnpzNz5sxsM4uM43V3dzfbhr29PS4uLqSnp+faT1ln3NXW3cUx23F3F6dy9yReREqH4gpulNdlW3kxziTNS1mcSVrk03qmTZtGzZo1TTtyGLVp04ZZs2bx+uuv88UXX/DZZ58xa9Ys4uLiWL9+PX/961+LemhSBlSUJGoiIiLWcPt21g90BwfzP/aNx5OTk/Ntq2/fvmzdupVFixbh5eVFp06dTGUBAQFs27YNgDt37pg9//z582zevNn0Z4PBQGRkJN26dTPNVjKOw9Ex9x/ZxllGeY05NjY21yV1ANu3b8+1rDTQrrYiUpyKa5lseV22lZfSNpM0r+/G2NhYateubVE7RR442rVrF+PGjcu1/JlnnmHu3LmmPz/77LPMmTOnqIclZYC2hxQRESkYW1tbUy6hvOSWQuBenTt3ZsiQISxbtozRo0fTsmVLPDw8iI6OJjo6msGDB7NixYpc0wt4eHiwf/9+bG1tOXjwIFOnTmXBggVcuHCBmTNnmsZrKUteV1mmXW1FpLgUV3CjvC7byo9xJmnOe1kn/Lu3LZP3skUeOEpLS+PChQu5lp8/fz7bkyp7e3vs7OyKelhSylW0JGoiIiLWUKVKFRITE0lNTTVbnpKS9VTXycnJovYmTJiAt7c3y5cv5+TJk5w/fx5vb28mTZqEg4MDK1aswMXFJdexGD377LM0btyYP//5z6xfv54xY8bQtGlTUx3juMwxvpa8xly7du1SP6tIRKQ0KY7gRkXeAKC0zCTN67sxr9lI9yvywFGHDh1YuXIl7du3x8/PL1tZUFAQK1eupGPHjqZjJ0+etHi6lJRPFTGJmoiIiDW4u7uTmJhIfHy82d9TxtxGueUUMqdXr1706tUrx/HAwEAga2aRJRo0aEDbtm3Zu3cvx48fp2nTpqZcTPHx5vNbpKSkcP36dWxsbEz5mURExDqKOrhR2pZtFbfyNJO0yANHEyZM4NVXX+Uvf/kL9evXp2HDhgCcOXOG33//nYcffpgPP/wQyHqidPLkSZ5//vmiHpaUYsW5PaSIiEh50rx5cyIjI/ntt99o06ZNjvLTp0+b6uXn0qVLREdHU69ePerVy/nkee/evQCmfhISEvjqq69ITk5m2rRpZts0zipPS0vLNg7juHIbb4MGDcrdjmoiIqVBUQc3yuOyrYqoyANH9erVY/369SxatIjg4GDTj4y6desybNgwRo0ahaurK5C1TC0gIKCohySlXEVMoiYiImINfn5+rF+/ni1bttC3b99sZdeuXWP//v3Y29tnm+2dm127djFx4kReeeUVpkyZkq0sLi6OrVu3UrlyZbp27QpkJd4OCAjgzp07DBw4EC8vr2znJCYmEhYWBmAqa9CgAY0aNSImJoajR4/mOGfTpk1AVr4lEREpm0rLsi15cMXyN1W9enXef/99/vOf/xAeHk54eDj/+c9/eP/9901BIxGjippETUREpLC6dOlC3bp1CQ4OZtWqVabjKSkpTJgwgeTkZPr165ft91daWhpRUVFERUWZZgJB1gYm9vb2rFu3jvDwcNPx69ev4+/vT0pKCv3796dOnTpAVk6jF198EciacX7v8rOrV6/yt7/9jcTERJ555hlatGhhKhsyZAgAEydOJCEhwXR87969LFu2DDs7O4YNG2adCyQiIiXCOLOpq1cDfBrVVNCojCnyGUciBVWRk6iJiIgUhoODAzNmzGDkyJFMmjSJgIAAPDw8OHz4MHFxcbRu3Rp/f/9s51y+fJmePXsCWUk0jTmLatasyfjx45k8eTKvvvoqjz/+OPb29hw8eJCbN2/y1FNP8e6772Zr6/333+fEiRMcPXqUrl274uPjQ2ZmJuHh4dy4cYNWrVrxz3/+M9s5AwYMYOfOnQQFBdG1a1c6dOjAjRs3CAkJITMzk88++8yUC0lERESKX7EGjm7dusWNGzfMbqdqfFolUtGTqImIiBRG+/btWb16NfPmzePAgQOcPn0aDw8P+vXrx/Dhw7PtdpafV199FVdXV7755htCQ0Oxs7OjcePGvPzyy7z44os5dsJ1dnbmu+++Y+nSpWzYsIGDBw9iY2PDI488Qq9evRg8eHCOc2xsbPjyyy9ZsWIFP/74I7t378bZ2RlfX19ef/112rVrZ5XrIiIiIg/GkJmZmVnUnaxdu5aFCxdy5syZXOucOHGiqIdRLhi3zKsIW84GR5xTEjUREbGaivQdWpHo71VERKTgCvL9WeQzjjZu3MgHH3zAI488Qv/+/Vm1ahW9evUiMzOTbdu20bhxY5599tmiHoaUQUqiJiIiIvKH9IwM/S4SESlHysrnepEHjr755htatGhBQEAAN2/eZNWqVfTt25eOHTty5swZ+vfvT+PGjYt6GFJGFfX2kCIiIiJlgfmZ2I74d/fRTGwRkTKoLH2uF3ko6/Tp0/z5z3/Gzs4Om/+PnBlzHDVs2JABAwawePHioh6GiIiIiEiZFBxxjvEBe3JsHBKXdJvxAXsIjjhXQiMTEZEHUdY+14s8cGRra4uzszMATk5OAFy7ds1U7uHhQXR0dFEPQ0RERESkzEnPyGDOptA868zZdJh0M5vPiIhI6VMWP9eLPHBUt25dLly4AICdnR116tRhz54/dsvav38/VatWLephiIiIiIiUOWFn43M8kb5fXFIyYWfji2lEIiJSGGXxc73Icxw98cQTbN68GX9/fwBeeOEF/vWvf3Hx4kUyMjIICQlh0KBBRT0MEREREZEy58rNFKvWExGRklUWP9eLPHA0cuRInn76ae7cuYOdnR1//etfuXbtGr/88gs2Njb07duXv/3tb0U9DBERERGRMqeGs4NV64mISMkqi5/rRR44cnd3x93d3fTnypUrM3nyZCZPnlzUXYuIiIiIlAoPuuWydwM33F0c81zW4O7ihHcDN2sOV0REikhZ/Fwv8sCRiIiIiEhFVpgtl21tbPDv7sP4gD251vHv3taiIJSIiJS8svi5bvXA0cGDBwFo3759tj/nx1i/MGJiYpg/fz6HDh0iISGBWrVq0aNHD0aPHk2VKlUK1NYvv/zCypUriYiIICMjg/r169OzZ0+GDRuGo6Njjvq3b9/mm2++YfPmzZw9e5aMjAw8PDzo0qULI0eOxMXFJcc5nTt35uLFi7mOYe7cuXTv3r1A4xYRERGR0sO45fL9jFsuT+vnm2/wyM+zHtP6+ZoJPjnh371tvueLiEjpUpDP9QedsWpNVg8cvfbaaxgMBsLCwrCzszP9OTeZmZkYDAZOnDhRqH7Dw8MZOnQoycnJeHt74+XlRWhoKAsWLGDHjh189913PPTQQxa1NXfuXL766itsbW157LHHcHZ2Jjw8nM8//5wNGzawcuVKqlWrZqqfmJjIa6+9RmRkJC4uLrRt2xZbW1uOHj3KwoULTUGomjVrms65evUqFy9epFq1ajz99NNmx1GnTp1CXRMRERERKTmWbrn8dIu6+d4E+HnW4+kWdUv85qG8Kg03ZiJSsVjyuV6YGavWZPXA0dSpUzEYDFSuXBmAadOmWbuLHNLS0hg3bhzJyclMnz6dl156CYCUlBT8/f3ZsWMHs2bNsiivUkhICF999RUuLi4sX76cFi1aAJCcnMxbb73Frl27mDt3LpMmTTKd89lnnxEZGUmHDh2YO3cu1atXByApKYm3336bXbt2MWnSJBYsWGA65/jx4wD4+voyc+ZMa10KERERESklCrLlsk+jmnnWg6zlDZbUk4IpLTdmIlLx5PW5bo0Zq9Zi9cBRnz59sv3ZGMQpShs3buTChQv4+vpm68/BwYGpU6fy7LPPsmbNGt5++22zS8bu9dNPPwEwYsQIU9AIwMnJyRQ42rlzp+l4SkoKGzZsAGD69OmmoBGAi4sLM2bMwNfXl19//ZXr169TtWpVACIiIgBo3bp1IV+9iIiIiJRGZXHL5YqmNN2YiYgYWXPGqjWUi/mXQUFBAHTt2jVHWfXq1enQoQNpaWns3r0737Y++eQT/vOf//Dqq6/mKEtPTwegUqU/4m0JCQm0atUKHx8fs0vLHn74YapWrUpGRgZXrlwxHTfOOFLgSERERKR8KotbLlcklt6YpWdkFNOIRESyFGTGanGw+oyj8ePHF/gcg8HA1KlTH7jPyMhIAJo3b262vGnTpgQFBXHq1Cl69uyZZ1uVKlXikUceyXH80qVLzJgxA4C+ffuajtetW5fvvvsu1/bOnj1LYmIitra2uLu7m44fP34cg8HApUuXGDZsGCdPniQ1NZXmzZszZMiQfMcpIiIiIqVbWdxyuSKx9lJCERFrKW0zVq0eODIu9bqXMTl2ZmZmjuPG5NiFCRxdvnwZIFvy6Xu5uWV9GcfFxRW47X/+858cOXKEI0eOYDAYGDlyJKNGjbL4fGP+Il9fX1Ny7sTERM6fPw/Au+++S8uWLWnfvj1nz57l8OHDHD58mEOHDvHRRx8VeLwiIiIiUjqUxS2XK5LSdmMmImJU2masWj1wdPLkyWx/TkhIYNSoUXh4eDBixAgaN24MwOnTp/n66685f/48S5YsKVSft29nPSlwcDB/0YzHk5OTC9x2YGAgiYmJANjZ2REXF0dCQgI1atTI99wFCxawZcsWHBwceOedd0zHjTvIOTk58fnnn9OpUydT2a+//oq/vz8rVqygbdu29OrVK0e7sbGxPPfcc7n2u337dktfnoiISLmS1/djbGwstWvXLsbRiBRsy2UpXqXtxkxExKi0zVi1euDoflOnTsXNzY0vvvgi2/FHH32UL774gtGjRzN16tRC7Sxma2tLhgVrj++f8WSJtWvXUr16dSIjI5k1axY///wz4eHhrFu3LtdAFcDcuXP56quvsLGxYerUqdmW0T3xxBPs3LmTO3fuUK9e9h8LnTp1YuzYsUyfPp1vv/3WbOBIRERERMoOS7ZcluJX2m7MRESMStuM1SIPHO3atYtx48blWt6pU6ccQaWCqlKlComJiaSmppotT0nJml7q5ORU4LaNTybbtGnD4sWL6du3L5GRkQQGBjJo0KAc9e/cucPEiRNZt24dlStXZvr06Tz//PPZ6hgMhlyX1UHW09Lp06dz/PhxMjIysLnvzVC7dm3NKhIRETEjr+/HvGYjiRS1vLZclpJR2m7MRETuVZpmrBZ54Oju3bumfD7mnD9/nrS0tEL14e7uTmJiIvHx8WanoBtzG92bnPpB2NnZ0aNHDyIjIzl27FiO8oSEBN544w0OHz6Ms7MzX3zxBb6+vgXup1atWkDWLm537tzJc2aTiIiIiIg8mNJ0YyYicr/SMmO1yANHHTt2ZOXKlTz22GM5nvRt27aNlStX8vTTTxeqj+bNmxMZGclvv/1GmzZtcpSfPn3aVC8/c+fOJSoqig8//NAUwLmXnZ0dkBUQu9fvv//OsGHDuHDhAnXr1mXhwoU0bdrUbB+rV69m7969+Pn58ec//zlH+aVLlwCoVq2agkYiIiIiIkWotNyYiYiYUxpmrBZ54GjChAkMHDiQN998k3r16tGoUSMAYmJiOHfuHDVr1uTDDz8sVB9+fn6sX7+eLVu20Ldv32xl165dY//+/djb29OxY8d829q3bx+hoaF4e3szYsSIHOW//vorAF5eXqZjly9fZsiQIcTGxuLl5cXChQt5+OGHc+3j8uXLbNy4kYsXL5oNHK1duxYgW9JsEREREREpGqXhxkxEpLQq8jB6nTp1+PnnnxkxYgSVKlVi79697N27l0qVKjFixAjWrl1L3bp1C9VHly5dqFu3LsHBwaxatcp0PCUlhQkTJpCcnEy/fv1wdXU1laWlpREVFUVUVFS2pXIDBw4EYN68eYSHh2erP3PmTA4cOICbmxt9+vQxlb377rvExsbSrFkzvv322zyDRgB9+vTBwcGBw4cPs2jRomxJuzdv3syiRYuws7NjzJgxD35RREREREREREQKyZD5IFuNlUIHDx5k5MiRpKSk0KpVKzw8PDh8+DBxcXG0bt2aZcuWUaVKFVP98+fPm5bObd++HQ8PD1PZxx9/zA8//ICNjQ1t27bFxcWFEydOcOnSJVxdXVm4cKFpSdyePXv4n//5HwDatWuX5za/b7/9NnXq1AFgw4YNfPDBB6SlpdGwYUOaN2/O+fPnOX78OJUrV+azzz6jR48eOdq4d8wiIiJiOX2Hlk/6exURESm4gnx/FvlSteLSvn17Vq9ezbx58zhw4ACnT5/Gw8ODfv36MXz48GxBo/x8+umndOzYke+++47jx49z584d6tSpw9ChQxk5cmS2JNtBQUGm/w8JCcmz3REjRpgCR7169aJRo0YsXryYgwcPsn37dqpXr06vXr0YPXq0RfmYRERERERERESKUrmZcVRR6KmaiIjIg9F3aPmkv1cREZGCK8j3p7YKEBERERERERERsxQ4EhERERERERERs6weOFq2bBkxMTHWblZERERERERERIqZ1QNH06ZN49ixY6Y/t2zZkvXr11u7GxERERERERERKWJWDxy5uLhw7do105+Ve1tEREREREREpGyqZO0G27Rpw7/+9S8uXryIs7MzAFu3buXs2bO5nmMwGHjjjTesPRQRERERESml0jMyCDsbz5WbKdRwdsC7gRu2NkrBKiJS2lg9cPTxxx/z3nvvsXTpUiArKLRlyxa2bNmS6zkKHImIiIiIVBzBEeeYsymUuKTbpmPuLo74d/fBz7NeCY5MRETuZ/XAUb169fj+++9JTU0lISGBZ599lg8//JDnnnvO2l2JiIiIiEgZExxxjvEBe3Icj0u6zfiAPUzr56vgkYhIKWL1wJGRvb09derU4aWXXsLb25u6desWVVciIiIiIlIGpGdkMGdTaJ515mw6zNMt6lq0bE3L3UREip7VA0fLli3j6aefplGjRkDWLmsiIiIiIiJhZ+OzLU8zJy4pmbCz8fg0qplnPS13ExEpHlYPx0+bNo1jx46Z/tyyZUvWr19v7W5ERERERAotPSOD0JjLbDl6ltCYy6RnZJT0kMq1KzdTrFLPuNzt/iCUcblbcMS5Bx6jiIhkZ/UZRy4uLly7ds3058zMTGt3ISIiIiJSaJqxUvxqODsUup61l7uJiEjerB44atOmDf/617+4ePEizs7OAGzdupWzZ8/meo52VRMRERGR4qQEzSXDu4Eb7i6OeS5Xc3dxwruBW67l1lzuJiIi+bN64Ojjjz/mvffeY+nSpUBWUGjLli1s2bIl13MUOBIRERGR4qIZKyXH1sYG/+4+ZoN2Rv7d2+Z53a213E1ERCxj9cBRvXr1+P7770lNTSUhIYFnn32WDz/8kOeee87aXYmIiIiIFJhmrJQsP896TOvna2aZoBP+3dvmO9PLGsvdRETEclYPHBnZ29tTp04dXnrpJby9valbt25RdSUiIiIiYjHNWCl5fp71eLpFXcLOxnPlZgo1nB3wbuBm0Qwvayx3ExERyxVZ4Mho2rRpAOzYsYPt27dz/vx5IGtmUpcuXfDz8yvqIYiIiIiImGjGSulga2PzQDO6rLHcTURELFfkgaO0tDTeeustgoODyczM5KGHHgJg//79BAYG4ufnxxdffEHlypWLeigiIiIiFUZMTAzz58/n0KFDJCQkUKtWLXr06MHo0aOpUqVKgdrau3cvX3/9NWFhYdy5c4d69erRu3dvBg4caNoM5V53795l1apVrFu3jqioKO7cuUOtWrXo1KkTo0ePpmbNnMGCwYMHc/DgwVzH8N577zFixIgCjTs3mrFS9hV2uZuIiFiuyANHCxYsICgoiMGDBzN69Gjc3d0BiI+PZ+HChaxYsYKFCxfy5ptvFvVQRERERCqE8PBwhg4dSnJyMt7e3nh5eREaGsqCBQvYsWMH3333nelhXn4WLVrErFmzAGjYsCGNGzcmKiqKWbNm8fPPP7No0SLq1Kljqn/nzh1GjhzJ/v37cXR0xMvLiypVqnDs2DFWrFjBxo0b+fbbb2nevLnpnMzMTCIiIrCzs6Nbt25mx9GkSZNCXJHsNGOlfCjMcjcREbFckQeO1q9fT7du3Zg4cWK2425ubkycOJH4+Hh+/vlnBY5ERERErCAtLY1x48aRnJzM9OnTeemllwBISUnB39+fHTt2MGvWLCZPnpxvWyEhIcyaNQuDwcBHH33EoEGDgKxAz5IlS5g5cybvvvsuK1aswGAwALBkyRL2799Ps2bNWLBggSnPZWpqKpMnT+bHH3/knXfeYf369aZ+YmJiuHXrFo8++igzZ8608hUxTzNWyocHXe4mIiKWK/JwfGxsLE888USu5R06dCA2NraohyEiIiJSIWzcuJELFy7g6+trChoBODg4MHXqVJycnFizZg1JSUn5tvX9998D0KdPH1PQCMBgMDBq1Ch8fX0JCQkhODjYVLZmzRoAJk2alG1zFHt7ez755BOqVq1KZGQkJ0+eNJVFREQA0Lp16wd70Q/Iz7MeP47rzfyhnfmkb0fmD+3Mj+N6KWgkIiJyjyIPHFWtWpXo6Ohcy2NiYqhatWpRD0NERESkQggKCgKga9euOcqqV69Ohw4dSEtLY/fu3fm2derUKQCee+45s+XGh4PGwFFKSgp169alcePGtGnTJkd9Ozs7PDw8ALh8+bLp+PHjx4HiDxzBHzNWuno1wKdRTS1zEhERuU+RfzN26tSJ77//no0bN+Yo++WXX1i1apV2VhMRERGxksjISIBsOYTu1bRpU+CPoFBe0tPTAXLNh1SpUlbWA+NDQgcHB5YvX84vv/yCnZ1djvo3b94kKioKgNq1a5uOGwNHycnJvP766zz11FN4e3vTp08fvvvuOzIyMvIdq4iIiBSNIs9x5O/vz969e3nnnXeYPXs2jRs3BrJ+YFy4cIE6deowbty4oh6G/L/0jAwlEBQRESnHjDN5zO1cBll5JgHi4uLybatx48ZER0dz4MABHn/88Rzlhw4dAiAhIcGisc2bN4+UlBSaNGlCs2bNgD8SYwN8+umnNGzYkLZt23LhwgVOnjzJJ598wp49e/jiiy+wtbW1qB8RERGxniIPHNWoUYO1a9eycOFCgoKC2LdvHwAeHh6MHDmSUaNG4eLiUtTDECA44pyZBJCO+Hf30Vp+ERGRcuL27azveQcHB7PlxuPJycn5ttW3b1+2bt3KokWL8PLyolOnTqaygIAAtm3bBmTtpJafdevWsXTpUmxsbPjwww9Nx8+dO8eNGzewtbVlypQp9OnTx1QWFhbGm2++ybZt21iyZAljxowx23ZsbGyuy+kAtm/fnu/4REREypu8vhtjY2Ozzf7NS5EHjgBcXFx49913effdd4ujOzEjOOKc2S1n45JuMz5gD9P6+Sp4JCIiUg7Y2tpatLQrMzMz3zqdO3dmyJAhLFu2jNGjR9OyZUs8PDyIjo4mOjqawYMHs2LFCtOStdwEBAQwadIkMjMzeeedd/D19TWV1a9fn71795KUlETDhg2zneft7c1HH33E2LFjTWMw7t4mIiIixaNYAkdSstIzMpizKTTPOnM2HebpFnW1bE1ERKSMq1KlComJiaSmppotT0lJAcDJycmi9iZMmIC3tzfLly/n5MmTnD9/Hm9vbyZNmoSDgwMrVqzIdfZ4RkYGs2fPZvHixQC8++67jBw5Mkc9V1dXXF1dzbbh5+eHra0tV65cITY2ljp16uSoU7t2bc0qEhERuU9e3415zUa6nwJHFUDY2fhsy9PMiUtKJuxsPD6NzOdDEBERkbLB3d2dxMRE4uPjzU5BN+Y2cnd3t7jNXr160atXrxzHAwMDAUw7pd0rOTmZv/3tb+zYsYPKlSszZcoUXnzxRYv7NLKzs8PV1ZX4+HjTMjwREREpPlYLHMXExHDkyBHi4uK4evUqqampVKtWDVdXVxo3boyPjw+Ojo7W6k4K4MrNFKvWExERkdKrefPmREZG8ttvv9GmTZsc5adPnzbVy8+lS5eIjo6mXr161KuXc0n73r17AXL0c/XqVUaMGEFERATVqlVj3rx5tG/f3mwf27ZtY9OmTbRu3Zphw4blKE9NTSUxMREbG5tcE36LiIhI0SlU4Ojw4cMEBASwa9eufHfTsLW1pVWrVvTu3ZsXXngh121dxfpqOJtPjvmg9URERKT08vPzY/369WzZsoW+fftmK7t27Rr79+/H3t6ejh075tvWrl27mDhxIq+88gpTpkzJVhYXF8fWrVupXLkyXbt2NR2/desWw4cP5+TJk9SvX5/FixfnyF10rxs3brB+/XpCQ0MZNGgQlStXzla+YcMG0tLSaNeuHc7OzhZcAREREbGmB0pos27dOnr37s3AgQP56aefuHLlCpmZmTg6OlK7dm1atmzJo48+SqNGjXB1dcVgMHD37l3CwsL4xz/+wTPPPMPEiROJjY219usRM7wbuOHukvdsL3cXJ7wbuBXTiERERKSodOnShbp16xIcHMyqVatMx1NSUpgwYQLJycn069cvW06htLQ0oqKiiIqKIi0tzXT8mWeewd7ennXr1hEeHm46fv36dfz9/UlJSaF///7Z8g5NmTKFkydP4u7uzsqVK/MMGgH86U9/okaNGly4cIFp06Zl6z8kJIRp06ZhMBh48803C3NZRERE5AEZMi3ZUuP/7d+/nxkzZnDixAkyMzOpWrUq3bp1o127dnh7e9OgQQOz5926dYtjx44RFhbGjh07OHLkCAD29vYMGTKEMWPG6AmShYwJrAqaADK3XdWMtKuaiIiUdw/6HVoWHTx4kJEjR5KSkkKrVq3w8PDg8OHDxMXF0bp1a5YtW0aVKlVM9c+fP5/t+tybs+j7779n8uTJVKpUiccffxx7e3sOHjzIzZs3eeqpp5g/fz4ODlmzlqOjo3n++efJyMigVatWPPLII7mOcdiwYbRu3RrI+o35+uuvk5ycTJ06dfD09OTq1ascPnwYgA8++MDsMjaoWH+vIiIi1lKQ788CLVUbOnQoAE899RQDBgygU6dOOaYTm1OlShU6dOhAhw4dGD16NOfOnWPdunWsWLGCJUuW4ODgwBtvvFGQoUgB+XnWY1o/X+ZsCs2WKNvdxQn/7m0VNBIRESlH2rdvz+rVq5k3bx4HDhzg9OnTeHh40K9fP4YPH54taJSfV199FVdXV7755htCQ0Oxs7OjcePGvPzyy7z44ovY2dmZ6u7cuZOMjAwAjh8/zvHjx3Ntt3v37qbAUYcOHVi7di0LFy5kz549/Prrrzg7O9O5c2dGjBhBu3btHvBKiIiISGEVaMbRyJEjGTt2LN7e3lbp/Pbt26xYsQInJycGDRpklTbLu8I+VUvPyCDsbDxXbqZQw9kB7wZu2No80IpFERGRMkUzU8on/b2KiIgUXJHNOFqyZMmDjSgXjo6OjBo1yiptxcTEMH/+fA4dOkRCQgK1atWiR48ejB49ukBP1QB++eUXVq5cSUREBBkZGdSvX5+ePXsybNiwXHeG++9//8vixYs5efIkKSkpPPLIIwwYMICXX34Zg8GQo/7du3cJDAzkhx9+4MyZM1SqVAkvLy9GjRrFE0888UDXwBK2Njb4NNKOJCIiIiIiIiKSv3Ix1SQ8PJw+ffqwfv163Nzc8PPzIzk5mQULFjBgwABu3LhhcVtz587F39+fw4cP07p1a5588kmuXr3K559/zssvv0xiYmKOc1auXMnw4cM5ePAgnp6edOjQgaioKCZOnMgHH3yQo35GRgbvvfceH3/8MefPn+fJJ5+kWbNm/Pe//2XYsGGsXr26MJdDRERERERERMQqCjTjKD937tzh/PnzZGZmUrt2bZycnKzZvFlpaWmMGzeO5ORkpk+fzksvvQRk7Rzi7+/Pjh07mDVrFpMnT863rZCQEL766itcXFxYvnw5LVq0ACA5OZm33nqLXbt2MXfuXCZNmmQ6Jzo6milTpuQ45+LFiwwdOpS1a9fSqVMnevbsaTonMDCQjRs30qpVK7755huqVq0KwN69exkzZgx///vf8fX1zbZDiYiIiIiIiIhIcbPKjKOzZ8/yxhtv4OPjw/PPP0+vXr147LHH6NGjBzNmzCAqKsoa3Zi1ceNGLly4gK+vryloBODg4MDUqVNxcnJizZo1JCUl5dvWTz/9BMCIESNMASAAJycn3nrrLSAr6eO9Fi9eTEZGRo5z6tSpw8cffwzA119/ne2chQsXAjBx4kRT0AigY8eODB06lNTUVFasWGHR6xcRERERERERKSqFDhydOXOGV155hR07dnD37l0yMzNN/8XExLB06VJ69+7NxIkTuXnzpjXGnE1QUBAAXbt2zVFWvXp1OnToQFpaGrt37863rU8++YT//Oc/vPrqqznK0tPTAahUKfskreDg4Fz7f/LJJ3FxceHo0aNcuXIFgNOnT3Pu3Dnc3Nzw8fHJcU737t2zvS4RERERkYoqPSOD0JjLbDl6ltCYy6T//659IiJSfAq9VG3OnDkkJSVRqVIlhgwZQufOnXF2diY+Pp5jx46xfft2jh8/TmBgIGFhYXz99de4ublZY+wAREZGAtC8eXOz5U2bNiUoKIhTp05lWy5mTqVKlXjkkUdyHL906RIzZswAoG/fvqbjV65c4erVq9jb29OoUaMc59na2vLII49w5MgRTp06RY0aNfIdb5MmTTAYDJw9e5bU1FTs7e3zHLOIiIiISHkUHHGOOZtCiUu6bTrm7uKIf3cf/DzrleDIREQqlkIHjg4cOIDBYOAf//gHL7zwgul4y5YteeaZZ/jrX//Kvn37mDhxIr/99ht//etf+eGHH7Cx0hbwly9fBqBmTfM7hRmDVHFxcQVu+5///CdHjhzhyJEjGAwGRo4cmW0XOGPfbm5uZndOu7f/+Pj4bOe4u7ubrW9vb4+LiwvXr18nISFBeY5EREREpMIJjjjH+IA9OY7HJd1mfMAepvXzVfBIRKSYFDpwlJSUhJ2dHb179861zhNPPEFAQAD9+/fn2LFjrF69mv79+xe2awBu3856AuHg4GC23Hg8OTm5wG0HBgaadlGzs7MjLi6OhIQEatSoka1vR0fHXNswzhi6detWtnFYck5uY46NjeW5557L9fzt27fnWiYiIlKe5fX9GBsbS+3atYtxNCLyINIzMpizKTTPOnM2HebpFnWxtdLDaBERyV2hP2kdHByws7PLdwaRq6srEyZMIDMz05SE2hpsbW0tqpeZmVngtteuXUtYWBirV6/Gx8eHn3/+mUGDBpGSkgJQoFlTxv4tHS9AhtZwi4iIiEgFE3Y2PtvyNHPikpIJOxtfTCMSEanYCj3jqF69epw6dYrff/+d+vXr51n36aefxs7Ojt9++62w3ZpUqVKFxMREUlNTzZYbgzxOTk4Fbtv4VLJNmzYsXryYvn37EhkZSWBgIIMGDaJKlSrZ+jDHOC5j/w9yjrlxaVaRiIhITnl9P+Y1G0lESo8rN3P/nfwg9UREpHAKPePopZdeIjMzk9mzZ+db19bWFgcHh1zzAT0IY64gYw6h+xlzG+WWU8hSdnZ29OjRA4Bjx44Bf+RVMu6YZkn/xnNyG29KSgrXr1/HxsbGqknERURERETKghrO5lNQPGg9EREpnEIHjgYNGkSzZs3YvHkzkydPJi0tLde6UVFRJCUl0aRJk8J2a2LcnSy3WUynT5/OVi8vc+fO5a233uLSpUtmy+3s7AC4e/cuANWqVaNmzZrcvn2bc+fO5aifnp5OdHQ0AM2aNcs2DuO4chtvgwYNtKOaiIiIiFQ43g3ccHfJPR8ogLuLE94N9JBVRKQ4FDpwVKlSJebOnYuLiws//PADPXv2ZPny5Vy4cCFbvdOnT/P2228DMHz48MJ2a+Ln5wfAli1bcpRdu3aN/fv3Y29vT8eOHfNta9++fWzevJmNGzeaLf/1118B8PLysqj/PXv2cOPGDVq1amWacdSgQQMaNWrExYsXOXr0aI5zNm3aBEDnzp3zHa+IiIiISHlja2ODf3efPOv4d2+rxNgiIsXEKp+2jRo14vvvv6dJkyacO3eOqVOn0qVLF5544gl69+7Ns88+S+/evYmNjWXq1Kl069bNGt0C0KVLF+rWrUtwcDCrVq0yHU9JSWHChAkkJyfTr18/XF1dTWVpaWlERUURFRWVbYbUwIEDAZg3bx7h4eHZ6s+cOZMDBw7g5uZGnz59sp1TqVIl/vWvf2U75+LFi/z9738H4PXXX8825iFDhgAwceJEEhISTMf37t3LsmXLsLOzY9iwYYW5LCIiIiIiZZafZz2m9fPNMfPI3cWJaf188fOsV0IjExGpeAyZD7LdWC7u3r1LYGAgq1at4sSJEzk7MxioUaMGrVq1omXLlnh6etKyZUs8PDwK1e/BgwcZOXIkKSkptGrVCg8PDw4fPkxcXBytW7dm2bJlpqTUAOfPnzclyNy+fXu2/j/++GN++OEHbGxsaNu2LS4uLpw4cYJLly7h6urKwoULadOmTbb+lyxZwmeffUalSpV4/PHHsbe3Z//+/SQnJzNgwAA++eSTbPUzMjL461//SlBQEM7OznTo0IEbN24QEhJCZmYmn332Gb179zb7Wu8dt4iIiFhO36Hlk/5ey7f0jAzCzsZz5WYKNZwd8G7gpplGIiJWUJDvT6sGju4VFxdHaGgox48fJyIiguPHj5OYmPhHx/ckyH7ooYdo2bIl33777QP3FxkZybx58zhw4ADJycl4eHjQo0cPhg8fjrOzc7a6eQWOAP7zn//w3Xffcfz4ce7cuUOdOnXw8/Nj5MiRuSbZ3r59O0uXLuX48eMYDAYaNWrEoEGDeOGFF7Ax8+WWlpbGihUr+PHHHzl79izOzs54enry+uuv065du1xfp34ciYiIPBh9h5ZP+nsVEREpuFIRODLn4sWLREREcOzYMSIiIoiIiDDtSGYwGMzOUpLs9ONIRETkwRTnd+i0adNo164df/rTn4q8r4pOv40kP5q1JCKSU0G+PysV9WDuVadOHerUqUOXLl1Mxy5fvmyakSQiIiJSHnz77bf88ssvChyJlLDgiHPM2RRKXNJt0zF3F0f8u/soT5KIiIVKPNRes2ZNOnfuzJtvvlnSQxERERGxmtwmdW/bto0zZ84U72BEKqDgiHOMD9iTLWgEEJd0m/EBewiOOFdCIxMRKVtKPHAkIiIiUpFMnjyZnj17lvQwRMq19IwM5mwKzbPOnE2HSc/IKKYRiYiUXQUKHC1ZsoSUlBSrDuDo0aP8+uuvVm1TREREpDQrxhSTIhVS2Nn4HDON7heXlEzY2fhiGpGISNlVoMDRzJkz6dKlC0uXLiUpKalQHYeEhDBmzBj69evH0aNHC9WWiIiIiIiI0ZWblj3strSeiEhFVqDk2GPGjOHbb79lxowZzJ49Gz8/P55//nnatWvHww8/nOe5aWlpnDhxgh07drBhwwYuXLhAZmYmbdq0yZYsW0REREREpDBqODtYtZ6ISEVWoMCRv78/AwcOZPbs2WzYsIEtW7awdetWAGrXrk3z5s1xdXWlatWq2NnZkZSUxPXr1zl37hwnT54kLS0NyJqeXb9+ff73f/+X559/3vqvSkREREREKizvBm64uzjmuVzN3cUJ7wZuxTgqEZGyqUCBI8jaBW3GjBm8/fbbBAQEEBgYyKVLl7h48SIXL17EYDDkOMe4jr9SpUp06tSJ/v378/TTT5utKyIiIlIepKWlcebMGRo2bFjSQxGpcGxtbPDv7sP4gD251vHv3hZbG+0VJCKSnwIHjoxq1qzJ2LFjGTt2LJGRkRw8eJDw8HDi4uK4evUqqampVKtWDVdXV5o0aUK7du3w8fHB2dnZmuMXERERKZWSkpLo0aMHVapUoVWrVrRu3RovLy/TDGwRKVp+nvWY1s+XOZtCs808cndxwr97W/w865Xg6EREyo4HDhzdq1mzZjRr1oxBgwZZozkRERGRMq127drExsYCcPPmTfbv38+BAwey1RkxYgQtWrTA09OTFi1a8Mgjj2g2toiV+XnW4+kWdQk7G8+VmynUcHbAu4GbZhqJiBSAVQJHIiIiIvKHoKAgrl27RkREBMePHzf9d/78eVOdPXv28N///tf0ZwcHB5o1a0bLli1N/7Vp06Ykhi9Srtja2ODTqGZJD0NEpMyyauAoNjaWixcv4ujoSP369bUsTURERCqs6tWr4+vri6+vr+lYUlISx48fJyIigoiICI4dO8bvv/9OZmYmt2/fJiwsjPDwcAAMBgMRERElNXwRERERwEqBo+joaD788EPCwsJMx/RjR0RERCQ7FxcXOnbsSMeOHU3Hbt68yYkTJ0yzkiIiIoiJiTFtLiIiIiJSkgodOIqLi2Pw4MFcu3aNzMxMHnroIW7cuJGjXmJiIuvWraNFixZ06NChsN2KiIiIlAvOzs60b9+e9u3bm47dvn2bEydOlOCoRERERLIUOivcokWLuHr1Kk2aNGHTpk0cPHiQGjVq5KhXrVo1duzYwbBhwzh58mRhuxUREREptxwdHfHx8SnpYYiIiIgUPnC0c+dODAYDkydPpmHDhnnWffHFF8nMzGTHjh2F7VZERERERERERIpYoQNHsbGxVKpUicceeyzfusYlanv27ClstyIiIiIiIiIiUsQKnePIwcGB9PR0i+rWqVMHOzs7zp49W9huRURERERERESkiBV6xlG9evW4ffs2CQkJFtV3dHTk+vXrhe1WRERERERERESKWKEDR08++SQA69aty7fu7du3SUpKwsam0N2KiIiIiIiIiEgRK3QEZ9CgQVSqVIlFixZx5syZPOvu27ePzMxMatasWdhuRUSknEnPyCA05jJbjp4lNOYy6RkZJT0kEREREZEKr9A5jmrXrs3777/PlClTGD58OB9//LHZeteuXWP69OkYDAaLEmmLiEjFERxxjjmbQolLum065u7iiH93H/w865XgyEREREREKrZCB44ABg8eTGZmJtOmTeOvf/2r6fjXX3+Nvb09Z8+eZf369Vy7dg1bW1sGDRpkjW5FRKQcCI44x/iAnLttxiXdZnzAHqb181XwSERERESkhFglcATw2muv0bJlS6ZNm8bx48cB+Oyzz0zlmZmZALz77ru0bt3aWt2KiEgZlp6RwZxNoXnWmbPpME+3qIttOcyPl56RQdjZeK7cTKGGswPeDdzK5esUERERkbLLaoEjgHbt2hEYGMihQ4cICgri1KlTJCQkYGNjQ7Nmzejfvz/e3t7W7FJERMqwsLPx2ZanmROXlEzY2Xh8GpWv/HhaniciIiIiZYFVA0dGjz32mPIYiYhIvq7cTLFqvbJCy/OkqMXExDB//nwOHTpEQkICtWrVokePHowePZoqVaoUqK29e/fy9ddfExYWxp07d6hXrx69e/dm4MCBODs756h/9+5dVq1axbp164iKiuLOnTvUqlWLTp06MXr0aLObpNy9e5fAwEB++OEHzpw5Q6VKlfDy8mLUqFE88cQTD3wdREREpPA0H15EREpMDWcHq9YrTXLbJc7S5XnaVU4eVHh4OH369GH9+vW4ubnh5+dHcnIyCxYsYMCAAdy4ccPithYtWsSwYcPYuXMn1atX58knn+TOnTvMmjWLAQMGcPHixWz179y5w//8z//w97//nd9++41WrVrx1FNPkZKSwooVK3jhhRc4depUtnMyMjJ47733+Pjjjzl//jxPPvkkzZo147///S/Dhg1j9erVVrkuIiIi8mCKZMaRiIiIJbwbuOHu4pjncjV3Fye8G7gV46gKL69laC6OdhV2eZ4UvbS0NMaNG0dycjLTp0/npZdeAiAlJQV/f3927NjBrFmzmDx5cr5thYSEMGvWLAwGAx999JFpc5PMzEyWLFnCzJkzeffdd1mxYgUGgwGAJUuWsH//fpo1a8aCBQuoW7cuAKmpqUyePJkff/yRd955h/Xr15v6CQwMZOPGjbRq1YpvvvmGqlWrAlkzncaMGcPf//53fH19qVOnjjUvlYiIiFhIM45ERKTE2NrY4N/dJ886/t3blqmE0cZlaPcHh4zL0HaeumBRO+VteZ4Uj40bN3LhwgV8fX1NQSMABwcHpk6dipOTE2vWrCEpKSnftr7//nsA+vTpk21HXIPBwKhRo/D19SUkJITg4GBT2Zo1awCYNGmSKWgEYG9vzyeffELVqlWJjIzk5MmTprKFCxcCMHHiRFPQCKBjx44MHTqU1NRUVqxYUcArISIiItbyQL/E4+LiGD9+PE899RRt2rShW7duzJkzh1u3buV5XkJCAgEBAYwePfqBBisiIqVTbsuyLOHnWY9p/Xxxd3HMdtzdxanM5fqxZBna5vCzFrVVFpfnSckLCgoCoGvXrjnKqlevTocOHUhLS2P37t35tmVcUvbcc8+ZLTfmHjIGjlJSUqhbty6NGzemTZs2Oerb2dnh4eEBwOXLlwE4ffo0586dw83NDR+fnEHk7t27Z3tdIiIiUvwKvFTt+vXrDBgwgNjYWDIzMwE4e/YsixYtYsuWLXz77be4u7ub6l+4cIHNmzezdetWwsPDyVDOBhGRcsUau4P5edbj6RZ1y/zW9JbsEpeYnEo1J3sSk1NzrVMWl+dJ6RAZGQlA8+bNzZY3bdrUtPNtz54982wrPT0dgIceeshseaVKWT8jo6OjgaxZTcuXL8+1vZs3bxIVFQVA7dq1LRpvkyZNMBgMnD17ltTUVOzt7fMcs4iIiFhfgX+Rf/3111y8eJHMzExatGjBiy++SIcOHTAYDJw5c4YPP/wQgH379jFkyBC6dOnCZ599xuHDh0lPTyczM9P0tElERMq2/JZlBUecAyybkWRrY4NPo5p09WqAT6OaZS5oBJYvL+vWpkGe5WVteZ6UHsaZPOZ2LgNwc8sKSMbFxeXbVuPGjQE4cOCA2fJDhw4BWTPKLTFv3jxSUlJo0qQJzZo1yzbeex863sve3h4XFxfS09Mt7kdERESsq8AzjoKCgjAYDAwaNIiJEyeajoeEhDB69Gj27NnDv//9b2bPnk1GRgaZmZlUqlSJxx57DD8/Pzp16sQjjzxi1RchIiLFz9LdwTIyM5m7+XChZiSVFZYuL3umeV0ere9mZqaWE/7d25a76yLF5/btrPeTg4P596LxeHJycr5t9e3bl61bt7Jo0SK8vLzo1KmTqSwgIIBt27YBWTup5WfdunUsXboUGxsb00PGe8fh6OiY26mmWUZ5jTk2NjbXJXUA27dvz3eMIiIi5U1e342xsbGmGcD5KXDg6Ny5rKfHb731Vrbj7dq1Y8yYMcyZM4fZs2eTnp5Oq1atGDJkCM899xzOzs4F7UpEREoxS5ZlxSUlM2H1f80cz5qRVNZyGOWnILvE2drYlIvleVK62NraWpQWwJhuIC+dO3dmyJAhLFu2jNGjR9OyZUs8PDyIjo4mOjqawYMHs2LFCtOStdwEBAQwadIkMjMzeeedd/D19c02Xksp3YGIiEjJKHDg6Pbt21StWhUXF5ccZS+//DJz5swhIyOD3r1789lnn1llkCIiUvpYY9evOZsO83SLuuUmWGLcJW58wJ5c69y7DM24PE/EWqpUqUJiYiKpqeZzaKWkZP27dXJysqi9CRMm4O3tzfLlyzl58iTnz5/H29ubSZMm4eDgwIoVK8z+JoSsQM/s2bNZvHgxAO+++y4jR47MMd57x2WO8bXkNebatWtrVpGIiMh98vpuzGs20v0KHDgCqFy5stnjDz/8MFWqVCE5OZm//OUvD9L0A4uJiWH+/PkcOnSIhIQEatWqRY8ePRg9erTpR4mlgoODWbFiBceOHePmzZtUrVqVxx57jJEjR2bbJeT8+fMWX+w333yTsWPHmv48ePBgDh48mGv99957jxEjRhRo3CIixckau37FJSUTdja+XAVPjLvEaRmalAR3d3cSExOJj483O/3cmNsot5xC5vTq1YtevXrlOB4YGAhgNndlcnIyf/vb39ixYweVK1dmypQpvPjiiznqGXMxxcfHm+07JSWF69evY2NjY8rPJCIiIsXrgQJHeXF0dCQ5OZn69etbu+lchYeHM3ToUJKTk/H29sbLy4vQ0FAWLFjAjh07+O6773LdEeR+s2fPZuHChRgMBlq1akWtWrWIjo5m8+bNbN++nX/84x+mHz5OTk707t0717auXr3Knj17MBgMeHp6mo5nZmYSERGBnZ0d3bp1M3tukyZNLL8AIiIlwJJlWZawxsyl0sZau8SlZ2RoKZsUSPPmzYmMjOS3337L9rDL6PTp06Z6+bl06RLR0dHUq1ePevVyBjz37t0LkKOfq1evMmLECCIiIqhWrRrz5s2jffv2uY733nHlNt4GDRpoRzUREZES8kCBo7S0NE6fPk3jxo0xGAzmG85nvbu1pKWlMW7cOJKTk5k+fTovvfQSkPWEyt/fnx07djBr1iwmT56cb1shISEsXLgQJycnFi9eTLt27Uxlq1atYtKkSXz88cc88cQT1KpVC1dXV2bOnGm2rfT0dIYNGwbAG2+8kW1mUkxMDLdu3eLRRx/N9XwRkdLOkmVZlrDGzKXSqLDL0IIjzpmZtVQ+k4qL9fj5+bF+/Xq2bNlC3759s5Vdu3aN/fv3Y29vT8eOHfNta9euXUycOJFXXnmFKVOmZCuLi4tj69atVK5cma5du5qO37p1i+HDh3Py5Enq16/P4sWLadiwYa59NGjQgEaNGhETE8PRo0fx8vLKVr5p0yYgK9+SiIiIlIwHemyZlJRE79698fHxYcCAAUyZMoW1a9fy22+/WZRs0Zo2btzIhQsX8PX1NQWNIGvXkKlTp+Lk5MSaNWtISkrKt601a9YAMHLkyGxBI4ABAwbQqVMnUlNT2bx5c75tffnllxw4cIDHH3+cN954I1tZREQEAK1bt863HRGR0sy4LMvdJfuOSO4uTvzj5SdzHL+fMVG0ZBcccY7xAXtyzOYyJhUPjjhXQiOT0q5Lly7UrVuX4OBgVq1aZTqekpLChAkTSE5Opl+/fri6uprK0tLSiIqKIioqirS0NNPxZ555Bnt7e9atW0d4eLjp+PXr1/H39yclJYX+/ftTp04dU9mUKVM4efIk7u7urFy5Ms+gkdGQIUMAmDhxIgkJCabje/fuZdmyZdjZ2ZkexomIiEjxK/C0oNq1axMbGwtkJco+cuQIYWFhOepNmzaNli1b0qJFC5o0aVJkM5CCgoIAsj3tMqpevTodOnQgKCiI3bt307NnzzzbcnBwoFmzZnTo0MFs+SOPPMKvv/5qyg+Qm6ioKJYsWYKjoyNTp07F5r5lBcePHwcUOLIWLeUQKVl5LcuysTFYnChasqRnZDBnU2iedcpbUnGxHgcHB2bMmMHIkSOZNGkSAQEBeHh4cPjwYeLi4mjdujX+/v7Zzrl8+bLpN9L27dtNOYtq1qzJ+PHjmTx5Mq+++iqPP/449vb2HDx4kJs3b/LUU0/x7rvvmtqJjo5m7dq1ALi5ufHPf/4z13EOGzbM9DtowIAB7Ny5k6CgILp27UqHDh24ceMGISEhZGZm8tlnn5lyIYmIiEjxK3A0JygoiGvXrhEREcHx48dN/50/fz5bvWXLlv3RSaVKNGnShBYtWtCyZUtatmyZ61r3goqMjARyX6vftGlTgoKCOHXqVL6Bo/yWsxkDZOaSTd7rH//4B2lpabzxxhtmcwIYA0fJycm8/vrrHDt2jBs3btC4cWNefvllBgwYkCPYJOZpKYdI6ZDbsiwlii64sLPx+eaNKo9JxcV62rdvz+rVq5k3bx4HDhzg9OnTeHh40K9fP4YPH16gTUNeffVVXF1d+eabbwgNDcXOzs70e+XFF1/Ezs7OVHfnzp1kZGQAmH4f5qZ79+6mwJGNjQ1ffvklK1as4Mcff2T37t04Ozvj6+vL66+/nmMWuIiIiBQvQ6aV1pYlJSVx/PhxIiIiiIiI4NixY/z+++/Zlq4Z8yEZDAbTcq3C8vHx4datWwQFBWWbKm20bNky/vGPf9CnTx+mTZv2wP3s2LGDv/zlL1SuXJlt27ZRq1Yts/X27dvH0KFDefjhh9m+fTuOjtmXaWRmZtK+fXtu3LgBQMOGDWnWrBkXLlzg5MmTpKen06VLF7744gtsbW1ztG/MlaQtZ/9YypGbaf18dVMqUkpoZqDlthw9y6TAvfnW+6RvR7p6NSiGEZUf+g4tn/T3KiIiUnAF+f602voxFxcXOnbsmC3Z4s2bNzlx4oTpqVNERAQxMTFWzYN0+3bWU1kHB/PJVY3Hk5OTH7iPU6dOMX78eCAr/1FuQSOAJUuWADBixIgcQSOAc+fOcePGDWxtbZkyZQp9+vQxlYWFhfHmm2+ybds2lixZwpgxY8z2ERsbmy3Z9v0qwg8nLeUQKVsKmyi6IrE0WXh5TSpeWHl9P8bGxuY7a1hEREREsivSrc+cnZ1p3759tmVpt2/f5sSJE1brw9bW1jQtOi8PGqwKDw9n9OjRJCYm0rlzZ8aOHZtr3dOnT7Nr1y6qVavGwIEDzdapX78+e/fuJSkpKUfCSG9vbz766CPGjh3LsmXLGD16dK671lV0WsohIuWVdwM33F0c8/yMU1JxERERESkuRRo4MsfR0REfHx+rtVelShUSExNJTU01W56SkgKAk5NTgdvetGkTH3zwAbdv36Zr167MmjXL7PIxow0bNgDQs2dPs7ONjFxdXbPtZnIvPz8/bG1tuXLlCrGxsWaX39WuXbtCzCrKy5WbKVatJyKlX0VZ7mZrY4N/dx8lFX9AeX0/5jUbSURERETMK1DgaMmSJQwePDjXZWEP4ujRo1y9epVOnTo90Pnu7u4kJiYSHx9vdvq5cQc0d3f3ArU7f/58vvzySzIzMxk8eDATJkzIN2H1li1bAPjzn/9coL7uZWdnh6urK/Hx8aZleJKTlnKIVCylLRF+UQexlFRcREREREqLAgWOZs6cydKlSxk5ciR9+vTBxcXlgTsOCQlh8eLF7Ny5kzfeeOOBA0fNmzcnMjKS3377jTZt2uQoP336tKmeJTIyMvjwww/56aefsLW15YMPPmDIkCH5nnfu3DmioqKoU6cObdu2zbXetm3b2LRpE61bt2bYsGE5ylNTU0lMTMTGxkZbz+ZBSzlEKo7cEuHHJd1mfMCeYk+EX1xBLD/Pejzdom6FmGUlIiIiIqVXgX59jhkzhps3bzJjxgyeeuop3nrrLTZv3kxCQkK+56alpREeHs7nn39Oly5deO211/j111/x8vKiS5cuD/wC/Pz8gD9m+9zr2rVr7N+/H3t7+2xJu/MyceJEfvrpJxwdHZk/f75FQSOAI0eOAOS7DO/GjRusX7+eZcuWkZaWlqN8w4YNpKWl4ePjg7Ozs0V9V0TGpRx50VIOkbLP0kT46RbkurMGYxDr/qC1MYgVHHHOqv0Zk4p39WqAT6Oa+kwTERERkWJXoBlH/v7+DBw4kNmzZ7Nhwwa2bNnC1q1bgay8O82bN8fV1ZWqVatiZ2dHUlIS169f59y5c5w8edIUKMnMzKR+/fr87//+L88//3yhXkCXLl2oW7cuwcHBrFq1igEDBgBZuY0mTJhAcnIyr732WracQmlpafz+++9AVrLqypUrA7B27VoCAwOxtbXlX//6l8XBJshacgfg5eWVZ70//elPzJw5kwsXLjBt2jTGjx9v6j8kJIRp06ZhMBh48803Lb8IFZSWcoiUf6UpEb52cxQRERGRiqjAybFr1qzJjBkzePvttwkICCAwMJBLly5x8eJFLl68aHYXMOOOZpUqVaJTp07079+fp59+2io7hjk4ODBjxgxGjhzJpEmTCAgIwMPDg8OHDxMXF0fr1q3x9/fPds7ly5fp2bMnkJVE08PDg/T0dD7//HMAatSoQWBgIIGBgWb7fPrpp3nhhReyHTt//jwAbm55L41ydnZm9uzZvP7666xcuZKgoCA8PT25evUqhw8fBuCDDz4oUNCqItNSDpHyrTQlwi9NQSxrqyiJx0VERESk4B54V7WaNWsyduxYxo4dS2RkJAcPHiQ8PJy4uDiuXr1Kamoq1apVw9XVlSZNmtCuXbsiW37Vvn17Vq9ezbx58zhw4ACnT5/Gw8ODfv36MXz4cKpUqZJvG6dOnSI2NhbICiytX78+17rVq1fPETi6evUqAA899FC+fXXo0IG1a9eycOFC9uzZw6+//oqzszOdO3dmxIgRtGvXLt825A/GpRwiUv6UVCJ8c4GU0hTEsqbSlnhcREREREqXBw4c3atZs2Y0a9aMQYMGWaO5Bx7DF198YVFdDw8PTp06le2Yp6dnjmMFsWrVqgLVb9CgAVOnTn3g/qR46Wm8SMkoiUT4uQVSXvBpbNH5ZWk3x9KWeFxERERESh+rBI5EyjM9jRcpOcZE+OaCG0bWTISfVyBlcfAxqjracf32nVzPL0u7OSpnk4iIiIhYQr8ERfJQ3DsolUfpGRmExlxmy9GzhMZcLrbdr6T8MCbCd3dxzHbc3cXJqjNiLAmkZObTRlnazbEgOZtEREREpOIq9IyjmJgYTpw4QZs2bfDw8LDGmERKBT2NLzzN1hJrKY5E+JYEUpJu32GUX2vWhUaV+d0cy2vOJhERERGxrkIHjtauXcuiRYt48cUXmTZtWo7y4OBgYmNjadq0qZI+S5lSnndQKg7KnSLWVtSJ8C0NkHg8/BA/jutd5vOelVTicREREREpWwodODpw4ABAjl3GAMaNG8fmzZtNf27bti3z5s3D1dW1sN2KFDk9jX9wmq0lpVluye4LEkgpD7s5lkTicREREREpewodOLp48SIArVu3znb80KFDbNq0CQBXV1du3rxJaGgof/3rXwu8A5lISdDT+Aen2VpSWuW1fPLpFnUrVCCluBOPi4iIiEjZVOhfg1evXqVKlSo4OztnO75u3ToA/Pz82LNnD9u2baNx48aEhYWxYcOGwnYrUuSMT+PzUp5uIq1Js7WkNMov2f2ukxfw7+6TZxvlLZBSXInHRURERKTsKvSv30qVKnH37t0cx4ODgzEYDPztb3/DYDDg7u6Ov78/mZmZChxJmWB8Gp+X8nYTaS2arSWlTUGWT1a0QIqfZz1+HNeb+UM780nfjswf2pkfx/Uql69VRERERAqu0EvV6tSpQ3R0NHFxcbi7uwNw8uRJ4uLiaNiwIU2bNjXVfeaZZzAYDBw/fryw3YoUC+PT+JxLW8reDkrFSblTpLQpyPLJ4tjBrbQpDzmbRERERKRoFDpw1K5dO6Kjo/n5558ZOXIkAGvWrAHgiSeeyFbXzs4OFxcXEhMTC9utSLGpiDeRhaXcKVLaFHT5pAIpIiIiIiJZCh04GjhwIGvWrOHLL7/k7t273L17l++//x6DwUC3bt1y1E9OTsbRMe+8MSKljW4iC06ztaQkWGPHtLImt9csIiIiImINhQ4cNW/enHHjxjFr1izmzp0LQGZmJo8++igdO3bMVvfs2bOkpaVRt27dwnYrImWAZmtJcaqIO6bl9ZrvDc5aI7ikAJWIiIhIxVTowBHAqFGjaNasGQEBAVy8eJFmzZrxzjvv5Ki3c+dOABo2bGiNbkWkDChNs7V041t+GXdMu59xx7Rp/XzL3fJJS16zn2c9i4NL+fVV2DZEREREpGyySuAIoFOnTnTq1CnPOj/++CMGgwEfn7x3qhIRsTbd+JZflu6Y9uO4XuVm+aSlrzkjM5MJq/+bo+z+4FJeLA1QiYiIiEj5ZLXAUX7u3LlD//79iYiI4Nlnny2ubkVEdONbzlXEHdMsfc2fbTyUZ505mw7zdIu6ub5+SwNUebUhIiIiImWb1QJHZ8+eZfPmzcTExJCZmYmbmxutWrXiqaeewtnZGTs7OwYMGGCt7kRELKIb3/KvIu6YZulrTkxOzbPcGFDL7XoUJChX1q+piIiIiJhnlcDR/Pnz+eqrr8jIyACykmMbDIasDipVom/fvvzlL3+hZk39qJTip7w2FZtufMu/8rxjWm6s+VryCkIVNChnCX0mi4iIiJQthQ4c/fzzz3z55ZcAVK5cmTZt2lCrVi2SkpI4duwY165d44cffmDDhg1Mnz6dLl26FHrQIpZSXhspihtfKV28G7iVyx3T8mLJa67mZJ/vjCPIOwhl7aCcJZ/JCiyJiIiIlC6FDhwtW7YMAE9PT7744gs8PDyyle/du5dFixaxd+9exo4dy5QpU+jbt29huxXJl/LaCFTM2SgVja2NTbnbMS0/lrzmd3s+xtwthwsVULNmUM6Sz2RAwX4RERGRUqbQv6JPnz6NwWBgxowZOYJGAB07duSbb75h4sSJAEyePJmzZ88WtluRPFma1yb9/5dXSvllvPHNS3mbjVIR+XnWY1o/3xx/1+4uTuU2SJzfa362dX38u+e9i2l+ATVjgKowbYBln8nT1h9kfMCeHEEqY2ApOOJcnueLiIiISNEo9Iwje3t7bGxsaNq0aZ71Bg8ezPnz51m6dCn//ve/+fTTTwvbtUiulNdGjCribBRrKytLh8rLjmkFkd9rNgaXcs7iccK/e1uLAmrWaMOSz+Sk23fyLFcSexEREZGSUejAkaenJwcOHODOnTvY2dnlWXfkyJEsXbqU3bt3F7ZbkTwpr43cyxo3vhVVWcsTVh52TCuo/F6zNQJqhW3DGp+1CvaLiIiIlIxCB4769+/P3r17+eWXX3jxxRfzrFujRg2qVq3KlStXCtutSJ6U10buVxFnoxSW8oSVH9YIqBWmDWt91irYLyIiIlL8ChQ4GjZsGJ6enrRq1QpPT08aNWpE9+7d6dKlCzNmzKBt27Y0aNAg1/Nv377NjRs3qF27dqEHLpKXirjLkuSvIs5GeVCW5gnT0iGxhCWfyZZQsF9ERESk+BXo1/6+ffv45ptveOedd+jZsyePPfYYr732GjVq1ODu3bv079+fH3/8kYxcEg4vXryYjIwMunXrZpXBi+TGWgldRSqqguQJE8mPJZ/JVR3zXu6uYL+IiIhIySjQjKOxY8dy4sQJIiIiuHjxIrdu3eLgwYOEhISY6kyYMIE5c+bQpUsX2rRpQ7Vq1bhy5Qrbt2/n119/xdvbm7feesvqL0TkfsprI/LglCdMrC2/z2RASexFRERESqECBY7eeOMN0/8nJiYSERHB8ePHiYiIICIigt9//53MzEzi4+NZtWoVq1atynZ+1apVeeGFF4iIiMDHJ+8njyLWoLw2FUdZ2fmrrFCeMCkK+X0mK9gvIiIiUvo8cHLsatWq8eSTT/Lkk0+ajt28eZOTJ09mCyZFR0eTnp4OZAWb/v73v2MwGIiIiCj86EUsoLw25V9Z2/mrLFCeMCkqeX0mK9gvIiIiUvoUele1ezk7O9OuXTvatWtnOpaampotmHT8+HFOnz5tzW5FpALTzl9Fw5iTRkuHpLgp2C8iIiJSulg1cGSOvb093t7eeHt7m47dvXu3qLsVKXZaKlX8tPNX0VKeMCkofQ6KiIiIlD9FHjgy22mlEulWpMhoqVTJKMjOX5rB8GC0dEgspc9BERERkfJJv/xFCsm4VOr+AIZxqVRwxLkSGlnRS8/IIDTmMluOniU05jLpGRnF2r92/ioexqVDXb0a4NOopoJGkkNF/hwUERERKe+sMvXn7t27/Pzzz4SGhnLnzh1q1apF06ZN8fT05JFHHsFgMFijG5FSpyIvlSoNswu085dIyavIn4OlWUxMDPPnz+fQoUMkJCRQq1YtevTowejRo6lSpUqB2tq7dy9ff/01YWFh3Llzh3r16tG7d28GDhyIs7NzvueHhITw2muv8f777zNs2DCzdd5//33Wrl2baxuDBg3i448/LtC4RURExDoKHThKTU1l6NChhIWFAZCZmZktUOTg4EDz5s3x9PTE09OTVq1a0bRpUy1Xk3Khoi6VKi0JqbXzl0jJq6ifg6VZeHg4Q4cOJTk5GW9vb7y8vAgNDWXBggXs2LGD7777joceesiithYtWsSsWbMAaNiwIY0bNyYqKopZs2bx888/s2jRIurUqZPr+TExMYwbN46MfGakHj9+HIDnn38eGzMBxjZt2lg0XhEREbG+Qkdvvv32W44cOYKtrS1//vOfcXZ2Zvny5aby27dvExYWZgosAVSuXJnw8PDCdp2NNZ+sBQcHs2LFCo4dO8bNmzepWrUqjz32GCNHjjT7w+WLL75g/vz5ubbn5+fHwoULsx1LSUlh2bJlrF+/nnPnzuHo6Ej79u35y1/+QsuWLQs0Xik5FXGpVGmaXaCdv0RKXkX8HCzN0tLSGDduHMnJyUyfPp2XXnoJyPrd4e/vz44dO5g1axaTJ0/Ot62QkBBmzZqFwWDgo48+YtCgQUDWQ8IlS5Ywc+ZM3n33XVasWGF2dvnevXt55513uHLlSp793L59m+joaNzc3Jg9e3bBX7SIiIgUqULfTW3atAmDwcA777zDtGnTmDBhAgA1atRg8+bN/O///i916tQhMzMTGxsbMjMzSUtLK/TA7xUeHk6fPn1Yv349bm5u+Pn5kZyczIIFCxgwYAA3btywuK3Zs2czZswYdu/eTd26denUqRMuLi5s3ryZV1991ew0auNTss6dO9O7d+8c/3Xo0CFb/ZSUFEaOHMmsWbNITEzkmWeewcPDg82bN/PKK6+wa9euQl0PKT4VcalUQWYXFAfjzl/uLo7Zjru7OBXbzCeRiqwifg6WZhs3buTChQv4+vqagkaQNQN86tSpODk5sWbNGpKSkvJt6/vvvwegT58+pqARgMFgYNSoUfj6+hISEkJwcHC28y5fvsxHH33E//zP/5CYmJjnjCSAkydPkp6eTuvWrQvwSkVERKS4FHrGUUxMDAAvv/xyjrIGDRrwl7/8hSFDhjB+/Hj27dvHzJkzsbOzK2y3JtZ+srZw4UKcnJxYvHgx7dq1M5WtWrWKSZMm8fHHH/PEE09Qq1YtU9nx48extbVlzpw5ODo6mms6m3/9618cPHiQZ555hi+//BIHh6wf0+vWreP999/n/fffZ8uWLRblDZCSVRGXSpXG2QXa+Uuk5FTEz8HSLCgoCICuXbvmKKtevTodOnQgKCiI3bt307NnzzzbOnXqFADPPfec2fInnniCPXv2EBwcTOfOnU3H58yZw08//UTjxo2ZMmUKAQEB/PTTT7n2Y3wAp8CRiIhI6VTou6r09HScnZ1zrJXPzMw0/X+VKlWYO3curVu35uOPP7bqDwNrPllbs2YNACNHjswWNAIYMGAAnTp1IjU1lc2bN5uOx8XFER8fT+PGjS0KGt26dYvly5dja2vLp59+agoaAbzwwgv07NmThIQE1q1bl29bUvKMS6XyUt6WSpXW2QXa+UukZFTEz8HSLDIyEoDmzZubLW/atCnwR1AoL+np6QC55kMy5quMjo7OdrxRo0ZMmTKFn3/+GR+fvN8b8EfgyNbWlr/97W907tyZNm3a8Pzzz7Nw4UJSU1PzbUNERESKTqF/xdWoUSPHMUdHR5KTk7MdMxgMjBs3jkuXLmXLgVRYljxZS0tLY/fu3fm25eDgQLNmzXIsLTN65JFHgKxgkVFBn5KFhIRw69YtvLy8qF27do7y7t27A3+8Lin9KtpSKePsgrxodoFIxVLRPgdLs8uXLwNQs6b5RORublmfzff+lslN48aNAThw4IDZ8kOHDgGQkJCQ7fiYMWN45ZVXLN4Ixfhb6vPPPyc0NJRWrVrh6enJ77//zuzZsxkyZEiO35UiIiJSfAq9VK127drExsZy69YtUxLq6tWrExsby/Xr16lataqpbps2bXB0dGTLli385S9/KWzXgGVP1oKCgjh16lS+U7LzW85mTPB9b8DH+GPHxcWFjz76iH379nHp0iVq1apFt27dGDNmTLYndcYnfLmNt0mTJtnqSdlQkZZKKSF1+ZKekVEh3rdS9CrS52Bpdvt21pLBe2c038t43JJATN++fdm6dSuLFi3Cy8uLTp06mcoCAgLYtm0bAHfu3Hng8aamphIVFQWAv78/o0ePNu2qFh0dzdixYzly5AjTp0/n008/zbWd2NjYXJfUAWzfvv2BxygiIlJW5fXdGBsba3YyizmFDhz5+PgQGhrK8ePHefzxxwFo1qwZsbGxhIaGZlvzbvT7778XtlsTaz5Zy8uOHTsIDQ2lcuXKdOnSxXTcGDhaunQprq6utG3bllq1anHs2DEWL17M1q1bWb58Oe7u7tnGYfzz/YzH89uBREof41KpisA4u2DOptBseU3cXZzw795WswvKiOCIc2b+Dh3x7+6jv0N5IBXpc7C0srW1JSMjI99696YUyE3nzp0ZMmQIy5YtY/To0bRs2RIPDw+io6OJjo5m8ODBrFixwuKZRebY29uzd+9e4uLiTA/PjB555BFmzJhB3759CQwM5L333lP+RxERkRJQ6MDRU089xeLFiwkODjYFjjp37kxwcDDffPNNtsBRSEgIt2/ftigXkKWs+WQtN6dOnWL8+PFAVv6jexNjR0REAPDqq6/y4YcfmhJ/X758mbfffpuQkBDGjx/Pv//972zjyO0a2NvbA5CRkZHrtdJTNSkNNLugbAuOOGd21lhc0m3GB+zR8iIps6z1ZK2sqlKlComJibnmBUpJydq4wMnJyaL2JkyYgLe3N8uXL+fkyZOcP38eb29vJk2ahIODAytWrMDFxaVQY3Zxccm1jdatW1OrVi0uXbrEiRMnaN++vdl6tWvX1u8fERGR++T13ZjXb6b7FTpw1L59e77//vtsM3r+/Oc/M3/+fA4ePMjw4cN5/vnnuXbtGkuXLsVgMNC2bdvCdmtizSdr5oSHhzN69GgSExPp3LkzY8eOzVZuTM7drFkzDAaD6XjNmjWZOXMmPXr0YPfu3URFRdG4cWNsbW0t7tuS1yVSkjS7oGxKz8hgzqbQPOvM2XSYp1vUVSBQpIxxd3cnMTGR+Ph4s0Gy/GY+m9OrVy969eqV43hgYCAAHh4eDzhay9SuXZtLly4pz5GIiEgJKXTgyMbGJkcgyNHRkVmzZjFq1Cj27t3Lvn37gKzgTeXKlXnrrbcK262JtZ+s3WvTpk188MEH3L59m65duzJr1qwcgR9nZ+dc8xXVrl0bT09PDh06xNGjR2ncuLEpD5RxXPczvg4bG5tcZyXpqZqIFEbY2fg8t04HiEtKJuxsvAKDUuZY68laWdW8eXMiIyP57bffaNOmTY7y06dPm+rl59KlS0RHR1OvXj3q1cs5A3Hv3r0AZvuxVEhICKtXr8bd3Z2//e1vZuvExsYClNhsMeWCExGRiq7IvvXat29PYGAgPXr0oEaNGjg7O9OhQwe+/fZbHn30Uav1Y3xiFh8fb7b8QZ6sAcyfP59x48Zx+/ZtBg8ezNy5c03L0ArC+CPH+JTMmIspt/EaczY9/PDDpuSQIiLWdOWm+cD1g9YTkdLDz88PgC1btuQou3btGvv378fe3p6OHTvm29auXbsYPnw4CxcuzFEWFxfH1q1bqVy5stmdbS2VkZHB2rVrWb58OTdu3MhRbtx0pE6dOjRt2vSB+3lQwRHn6PP5et74NohJgXt549sg+ny+nuCIc8U+FhERkZJSpJGJxo0bM2fOHHbt2sXBgwf59ttv8fHxsWofxidmv/32m9nygjxZg6wfMB988AFffPEFNjY2TJgwgY8++shsEOf06dOMHz+eCRMm5Nre/U/JjOMwjquw4xURKagazuZzwj1oPREpPbp06ULdunUJDg5m1apVpuMpKSlMmDCB5ORk+vXrh6urq6ksLS2NqKgooqKiSEtLMx1/5plnsLe3Z926dYSHh5uOX79+HX9/f1JSUujfvz916tR54PG2a9eOZs2acfv2bcaPH59tOdpvv/3GBx98AMCbb76ZLSVAcTDmgrt/hqYxF5yCRyIiUlEUeqlaQR05coS0tLRckxsWlJ+fH+vXr2fLli307ds3W1lBn6wBTJw4kZ9++glHR0fmzJljdlc4IwcHB3788UcARo0aRcOGDbOVnzlzhiNHjuDk5GR6vY899hjOzs4cOXKEy5cv59gNbtOmTQB59isiAg++fMK7gRvuLo55Lldzd3HCu4Hb/7V3nwFRXO3bwC96CWJQQSPYdUGKBFRQsYAFS2LUWB6NghqVGNPE6F997LHExBgsMYqoicaSqFhQDFhJYomKIPpYgyCCKFVUBAR35/3AuxPW3YUFF4Hl+n3SmTMzZ88uO/eeOec+2qwuEb0Gpqam+OabbzBx4kQsWLAAu3fvhp2dHWJjY5Geng5nZ2cEBgYqHJOWloYBAwYAKJ7qJ89Z1LBhQ8yePRsLFy7EqFGj4OHhARMTE1y8eBG5ubno2rUrZsyY8Ur11dfXR1BQEPz9/XHs2DH07t0brq6uyM/PR3R0NIqKiuDv768U41U25oIjIiL6V7nvdA4ODujWrVuFL/jpp59i7NixFT7+Zdp8snbgwAGEhobCwMAA69evL7Pzxs7ODj169AAAzJo1C9nZ2eK+hw8f4vPPP4dUKsX48ePF5WNNTEwwcuRIFBUVYfbs2Xj27Jl4TFhYGCIiIlC/fn0MGzbs1RqGiHTaq0yfMNDXR2C/0kd/BvZz448hohqqY8eO2LNnD/r27YvU1FRERUWhTp06+PTTT7F161Yx36ImRo0ahTVr1sDFxQUxMTG4dOkSWrVqhcWLF2P9+vVqV7Utj9atW+PgwYPw8/ODubk5/vrrL1y7dg0dO3bEjz/+WOrI7spSnlxwREREuk5PKOdyYw4ODmjQoAFOnz5doQt27doVWVlZuHHjRoWOV+XixYuYOHEiCgoK4OTkpPRkbdu2bQpBUkpKipggU/5kTSqVolevXnjw4AEaNmwIDw8Ptdfr1q0bBg0aBKB4jr+fnx/u3r2LOnXqiInCL1y4gIKCAvTt2xfff/89DA3/HdyVn58PPz8/XL16FfXr10eHDh3w8OFDxMXFwcTEBCEhIfD09FR57ZL1JqLaST59Qp2vR3jB21E5ka2q8wRFxCj8OLKxNEdgPzeNjieqaXgP1U2V8b4evZqEBaHnyiy3aGhn+Lo009p1iYiIXpfy3D+1PlWtqKgIRkZG2j5tqeRP1n744QdcuHAB8fHxsLOzw4gRIzB+/HiNnqzdunVLzEeUlpaGQ4cOqS1rZWUldhzZ2NggNDQUmzZtwtGjR/H333/DyMgIjo6OGD58OIYMGaI0J9/MzAzbtm1DSEgIjhw5glOnTsHKygp9+/bFlClT4ODg8AqtQVSzcLWa8tHm9Alvxybo5mDL9icieglzwREREf1Lqx1H+/fvx8qVK7F06VJxCtfrIpFIsGbNGo3K2tnZ4datWwrbHB0dlbZpysLCAlOnTsXUqVM1Psbc3BxffPEFvvjiiwpdk0gXqB7xYobAfu5VMuKlJnRilWf6hHuLhqWWA4qnrWlSjoioNmEuOCIion9preNo48aNCAoKgiAI+PjjjzFhwgQEBgZySXkiUknddCv5ajWaTrfSZn2qUyeWOpm5BRqXqwkdYURE1ZE8F1xp04KZC46IiGoLrdztli5dKnYaNWvWDDKZDJs2bYKfnx/S0tK0cQki0iGaTreSymSvpT41acllTadFpGQ9rXDybCIiKp7O+/UIL9hYmilst7E0f+0PN4iIiKrSK3UcFRUVYdq0adi+fTsEQcDUqVMRGRmJqVOnQl9fHzExMRg8eHCFE2kTkW6qTqvVVLdOrLLIp0+UxtLMGCFR/6sRHWFERNWZt2MT7Js6EOvG+mDR0M5YN9YH+6a+y04jIiKqVSrccZSXl4eAgAD8/vvvMDAwwJIlSzB58mQAwOTJk7FlyxbUr18fjx49QkBAAFatWoVyLuBGRDqqPNOtgOLOnZjENBy9moSYxDStduJUp04sTcinT5RGr9S91asjjIioupPngvN1aQb3Fg05PY2IiGqdCuU4KiwsxJgxY3D9+nWYmZlh1apVSsmwPT09ceDAAQQGBuLixYsIDg7GpUuX8Pz5c61UnIhqrvKsVlPZuYfK24lVHcinTyi3izkGubdESNT/Sj2+PMmziYiIiIiodqtQx9HTp09x/fp1WFlZITg4GO3atVNZrkGDBti6dStWrVqFkJAQREdHQxAEpeXpiah20XS1mpy855iz56zSPm0m0K6pSy57OzZBNwdbpeTXJ65pNg2tOnWEERERERFR9VWhsbaCIMDW1ha7du1S22kkXkBfH9OmTcOGDRtgaWlZoUoSkW7RZLrVF75vY3VkbKlltDHlSpOcQdV1yWVV0ydqakcYERERERFVT+UecfTpp58CAP7zn//A2lrzH1I9evTAgQMHEBoaWt5LEpEOKm26VWA/N1iaGWuce+hVplzp2pLLmo7mqo4dYUREREREVP1UuOOoIt56661XOp6IdIu66VYG+vo4ejVJo3NoY8pVWZ1YNWn1HF3rCCMiIiIioqpVoRxHRETaIp9u9bLXPeWqtE6sqiCVySpcF13qCCMiIiIioqrFjiMiqpaqYsqVuk6s100bK8lVt44wIiIiIiKqmfgLgoiqJU0SaOvilKuo68mYvfuMUoeZfCW5qOuarZoGqE6eTUREREREVB78FUFE1ZZ8ytXLq57ZWJrj6xFeOjflSiqTISgiptQy2lhJjoiIiIiISFOcqkZE1VptmnIVl5TxWlaSIyIiIiIi0hQ7joio2qsuuYcqm6YrxGljJTkiIiIiIiJN6N4jeyKiGup1ryRHRERERERUFnYcERFVE/KV5Eqj7ZXkiIiIiIiISsOOIyKiaqK2riRHRERERETVF399EKF4NauYxDQcvZqEmMQ0rlpFVaa2rSRHRERERETVG5NjU60XdT0ZQRExCqtZ2ViaIbCfO3+kU5WoTSvJERERERFR9caOI6rVoq4nY/buM0rb05/kY/buMxzhQVWmtqwkR0RERERE1RsfX1OtJZXJEBQRU2qZoIhYTlsjIiIiIiKiWosdR1RrxSVlKExPUyX9SR7ikjJeU42oJmA+LCIiIiIiqk04VY1qrczcAq2WI93HfFhERERERFTbcMQR1VoNLEy1Wo50mzwf1suj1OT5sKKuJ1dRzYiIiIiIiCoPO46o1nJtZq205PnLbCzN4drM+jXViKor5sMiIiIiIqLaih1HVGsZ6OsjsJ97qWUC+7lxCXRiPiwiIiIiIqq1+IuYajVvxyb4eoSX0sgjG0tzfD3Ci3lrCADzYRERERERUe3F5NhU63k7NkE3B1vEJWUgM7cADSxM4drMmiONSMR8WEREREREVFux44gIxdPW3Fs0rOpqUDUlz4dV2nQ15sMiIiIiIiJdxCEVRERlKG8+LKlMhpjENBy9moSYxDQmzSYiIiIiohqLI46IiDQgz4cVFBGjMPLIxtIcgf3cxHxYUdeTVZQxQ2A/d+bMIiIiIiKiGocdR0SvkVQmYy6lGqysfFhR15Mxe/cZpePSn+Rj9u4zTLhOREREREQ1DjuOiF4TjkTRDeryYUllMgRFxJR6bFBELLo52LKzkIiIiIiIagyd6jhKTEzEunXrcOnSJWRlZaFRo0bo378/AgIC8MYbb5TrXFFRUdi+fTv+97//ITc3F3Xr1kX79u0xceJEtGvXTql8fn4+fvrpJ0RGRiIpKQkymQx2dnbo3bs3Jk6cCEtLS6VjfHx8kJqaqrYOq1evRr9+/cpVb6qeOBJF98UlZZSaPBsA0p/kIS4pg4nYiYiIiIioxtCZjqMrV65g7NixyMvLg6urK1xcXBATE4MNGzbg5MmT2LlzJ+rUqaPRub7//nsEBwdDT08PTk5OaNSoERISEhAZGYkTJ05g6dKlGDx4sFg+JycHfn5+uH37NiwtLeHm5gYDAwNcvXoVwcHBOHLkCHbs2IGGDf/9sZidnY3U1FS8+eab6Natm8p6NG7c+JXahKoHjkSpHTJzC7RajoiIiIiIqDrQiY6joqIiTJ06FXl5eVi+fDmGDBkCACgoKEBgYCBOnjyJlStXYuHChWWeKzo6GsHBwTA3N0dISAg6dOgg7vv111+xYMECzJ8/H506dUKjRo0AACtWrMDt27fh6emJ1atXw8rKCgDw5MkTTJs2DX/99RcWLFiADRs2iOe6du0aAMDLywvfffedtpqCqiGORKkdGliYalyOua6IiIiIiKim0IlfKuHh4bh//z68vLzETiMAMDU1xbJly2Bubo69e/fiyZMnZZ5r7969AICJEycqdBoBwMiRI9GjRw88f/4ckZGRAIo7pw4fPgwAWL58udhpBACWlpb45ptvoKenhz/++AOPHz8W912/fh0A4OzsXMFXTTUFR6LUDq7NrGFjaVZqGRtLc+TkPcf7qw7hk62nsCD0HD7ZegrvrzqEqOvJr6mmREREREREmtOJjqNTp04BAHx9fZX2WVlZwdPTE0VFRTh9+nSZ5zI1NYVEIoGnp6fK/S1btgQApKenAwCysrLg5OQEd3d3lVPL6tevj7p160ImkyEzM1PcLh9xxI4j3VeekShUcxno6yOwn3upZfo4N8WcPWeVRqDJc12x84iIiIiIiKobneg4un37NgDA3t5e5f42bdoAAG7dulXmuRYuXIhDhw4pjTaSi4uLAwC89dZbAABbW1vs3LkTu3btUlk+KSkJOTk5MDAwgI2Njbj92rVr0NPTw8OHDzFu3Dh06tQJbm5uGDlyJI4cOVJmPanm0HQkimsz69dUI6os3o5N8PUIL6X328bSHEuHdcGx/yWVenxQRCykMlllVpGIiIiIiKhcdCLHUVpaGgAoJJ8uydq6+Ae5fJRQRZ08eRIxMTEwMjJC7969NTpGnr/Iy8tLTM6dk5ODlJQUAMCMGTPQtm1bdOzYEUlJSYiNjUVsbCwuXbqEefPmvVJ9qXqQj0RRtaqaXGA/N+a40RHejk3QzcFWKYcRc10R0eukzZVmz507hy1btiAuLg6FhYVo0qQJBg4ciA8++AAWFhZlHh8dHQ0/Pz/MnDkT48aNU1vuyJEj2LZtGxISEiCVSuHg4AB/f3/07du3XPUlIiIi7dKJjqP8/OIfY6amqqf6yLfn5eVV+Bq3bt3C7NmzARTnP5Inxi7Nhg0bcPToUZiammL69Oni9hs3bgAAzM3NsWrVKvTo0UPc98cffyAwMBDbt2+Hm5sb3n33XaXzPnjwAL169VJ73RMnTmj8uuj1kI9ECYqIUeg8sLE0R2A/N3g7NqnC2pG2GejrK3X+MNcV0etR2v3xwYMH4ohhXabNlWY3btyIlStXAgCaN2+OVq1a4c6dO1i5ciXCwsKwcePGUleBTUxMxNSpUyErYzTlt99+i82bN8Pc3Byenp4oLCzEhQsX8Pnnn2PKlCn44osvNG8AIiIi0iqd6DgyMDAoMyABAEEQKnT+K1euICAgADk5OfDx8cFnn31W5jGrV6/Gjz/+CH19fSxbtkxhGl2nTp3w559/ik/tSurRowc+++wzLF++HFu3blXZcUQ1k7qRKBxpVDsw1xURvQ7aXml25cqV0NPTw7x58zB69GgAxfHUpk2b8N1332HGjBnYvn079PT0lI4/d+4cpk+frpDjUZWzZ89i8+bNsLW1xfbt28WOqJs3b2LcuHH48ccf4e3tDVdX13K2BhEREWmDTnQcvfHGG8jJycHz589V7i8oKH6Cb25uXu5zR0REYNasWcjPz4evry9WrlwJAwMDteULCwsxd+5cHDx4EEZGRli+fDneeecdhTJ6enpqp9UBxU9Lly9fjmvXrkEmk0H/pY6Ft956i6OKaihVI1GodpDnuiptuhpzXRG9utLuj6WNRtIVZa0027NnT+zduxfTpk2DpaVlqeeS5298//33xU4joDiOmTRpEs6dO4czZ84gKioKPj4+4v60tDT88MMP2Lt3L/T19dG4cWOkpqaqvc6GDRsAAIGBgQqjlxwcHDB16lQsWLAAW7ZswerVq8vXGERERKQVOjHUQZ50OiMjQ+V+eW6jksmpNbFu3TpMnToV+fn5GDNmDFavXg1jY2O15bOysuDv74+DBw/CwsICwcHBFRoxJJ8GJ5VKUVhYWO7jiaj60WTVNea6IqJXpc2VZuWLiqjrcOvUqRMAICoqSmF7UFAQdu/ejRYtWuCXX35Ru1ItAOTm5iI6OhpGRkbo2bOn0n5fX1/o6enhzz//1Gh0OREREWmfTvxCkU8D++eff1Tuj4+PVyhXFplMhlmzZmHNmjXQ19fHnDlzMG/ePKWRPyXdu3cPw4cPR2xsLGxtbfHrr7/Cy8tLZdk9e/Zg2rRpCAsLU7n/4cOHAIA333xTbd4mIqp5Slt17esRXsx1RUSvTJsrzUqlUgBQmw/J0LB44HpCQoLC9hYtWmDJkiUICwuDu3vpHeZ37tyBVCqFra2tyqTd9erVQ4MGDZCXl4d79+6VWWciIiLSPp2Yqubt7Y1Dhw7h6NGjGDp0qMK+R48e4fz58zAxMUHnzp01Ot/cuXOxf/9+mJmZISgoSGH4tSppaWnw9/fHgwcP4OLiguDgYNSvX7/U8uHh4UhNTcV7772ntP/AgQMAoJA0m4h0A3NdEVFl0uZKs61atUJCQgIuXLgADw8Ppf2XLl0CUDziuqSPPvpIa/WV1zkjIwMZGRlo3ry5xucmIiIi7dCJjqPevXvD1tYWUVFR+PXXXzFy5EgAxbmN5syZg7y8PPj5+aFevXriMUVFReKTq6ZNm8LIyAhAcadNaGgoDAwMsH79eo06m2bMmIEHDx5AIpFg69atZS5z+/777yMkJASxsbHYuHEjJk2aJCaVjIyMxMaNG2FsbFyuwIt0h1QmY6eCjmOuKyKqLNpcaXbo0KE4duwYNm7cCBcXF4UHWrt378bx48cB4JWm1cvrYWZmpraMiYlJmXXmirNERETKtLXarE50HJmamuKbb77BxIkTsWDBAuzevRt2dnaIjY1Feno6nJ2dERgYqHBMWloaBgwYAKA4mLCzs4NUKsWqVasAAA0aNEBoaChCQ0NVXrNbt24YNGgQzpw5g/PnzwMALC0tsWDBArX1nDZtGho3bozGjRtj6dKlmDVrFlauXInQ0FDY29sjJSUF165dg5GREb799lu0atVKC61DNUnU9WQERcQoJFC2sTRDYD93TmMiIqIyaXOlWR8fH/j7+2Pbtm0ICAhA27ZtYWdnh4SEBCQkJGDMmDHYvn27OGWtovXVFHMcERERVQ2d6DgCgI4dO2LPnj344YcfcOHCBcTHx8POzg4jRozA+PHjyxwFBBTP93/w4AGA4o6lQ4cOqS1rZWWFQYMGiUkogeJla0szYcIEcbWQd999Fy1atEBISAguXryIEydOwMrKCu+++y4CAgI0zsdEuiPqejJm7z6jtD39ST5m7z7DHDhERFQmba80O2fOHLi6uuKXX37BzZs3kZKSAldXVyxYsACmpqbYvn17mauzlVXfkvVSRf5aSqszV5wlIiJSpq3VZnWm4wgAJBIJ1qxZo1FZOzs7pcSQjo6OGiWLLGnu3LmYO3duuY6Rc3JyEkc4Ue0mlckQFBFTapmgiFh0c7DltDUiIlLLxsYGOTk5yMjIUDn8vCIrzb777rsqV4mVj8q2s7OrYG3/zW2kbmVcoOKr4xIREZF28BcoUTUQl5ShMD1NlfQneYhLUh9YExERaXOl2YcPH+Ls2bNITk5Wuf/cuXMAgHbt2lWkqgCA1q1bw9DQEMnJySpHSWVnZyMrKwtmZmZo2rRpha9DREREFceOI6JqIDNX/RD9ipQjIqLaydvbGwBw9OhRpX3lXWn2r7/+wvjx4xEcHKy0Lz09HceOHYORkRF8fX0rXF8TExN06tQJhYWFCtP/5SIjIyEIArp3716ufEhERESkPew4IqoGGlioXv2mouWIiKh2enmlWbmSK82OGDFCaaXZO3fu4M6dOygqKhK3d+/eHSYmJjh48CCuXLkibn/8+DECAwNRUFCA//znP2L+xory9/cHACxfvhxJSUni9ps3b2L16tUAgICAgFe6BhEREVWcTuU4IqqpXJtZw8bSrNTpajaW5nBtZv0aa0VERDWNtlaaBYrzD82ePRsLFy7EqFGj4OHhARMTE1y8eBG5ubno2rUrZsyY8cp17tGjBz744APs3LkTAwcORKdOnSCVSnH+/HkUFRXhyy+/hLOz8ytfh4iIiCqGHUdE1YCBvj4C+7mrXFVNLrCfGxNjExFRmbSx0qzcqFGjUK9ePfz000+IiYmBsbExWrVqhWHDhmHw4MEwNjbWSp3nz58PZ2dn7Nq1CxcuXICJiQnefvttjB8/vlyrvhAREZH26QmCIFR1JUhz8uCJS87qpqjryQiKiFEYeWRjaY7Afm7wdmxShTUjIqr5eA/VTXxfiYiIyq8890+OOCKqRrwdm6Cbgy3ikjKQmVuABhamcG1mzZFGREREREREVCXYcURUzRjo68O9RcOqrgYRERERERERV1UjIiIiIiIiIiLVOOKISAdJZTJOdyMiIiIiIqJXxo4jIh2jOsG2GQL7uTPBNhEREREREZULhyAQ6ZCo68mYvfuMQqcRAKQ/ycfs3WcQdT25impGRERERERENRE7joh0hFQmQ1BETKllgiJiIZXJND5fTGIajl5NQkximsbHERERERERke7gVDUiHRGXlKE00uhl6U/yEJeUUeaqbZzuRkRERERERABHHBHpjMzcAq2U43Q3IiIiIiIikmPHEZGOaGBh+srltD3djYiIiIiIiGo2dhwR6QjXZtawsTQrtYyNpTlcm1mr3V+e6W5ERERERESk+9hxRFTDqEtabaCvj8B+7qUeG9jPDQb66v/stTXdjYiIiIiIiHQDk2MT1SBlJa32dmyCr0d4qShjjsB+bmUmttbGdDciIiIiIiLSHew4Iqoh5EmrXyZPWv31CC+x86ibgy3ikjKQmVuABhamcG1mXepIIzn5dLfSpquVNd2NiIiIiIiIdAenqhHVAOVNWm2grw/3Fg3h69IM7i0aatRpJD/uVae7ERERERERke7grz+iGuB1Jq2WT3d7OdG2jaW5OKqJiIiIiIiIagdOVSOqAV530upXme5GREREREREuoMdR0Q1QFUkrZZPdyMiIiIiIqLai8MHiGoAedLq0jBpNREREREREWkbO46IaoDKSFotlckQk5iGo1eTEJOYJibWJiIiovLhPZWIiHQZp6oR1RDypNVBETEKibJtLM0R2M+tXEmro64nqziPGQL7uTP5NRERUTnwnkpERLqOHUdENYg2klZHXU/G7N1nlLanP8nH7N1nuHIaERGRhnhPJSKi2oAdR0Q1zKskrZbKZAiKiCm1TFBELLo52HIFNSIiolLwnkpERLUF72JEtUhcUobCUHpV0p/kIS4p4zXViIiIqGbiPZWIiGoLdhwR1SKZuQVaLUdERFRb8Z5KRES1BTuOiGqRBhamWi1HRERUW/GeSkREtQU7johqEddm1rCxNCu1jI2lOVybWb+mGhEREdVMvKcSEVFtwY4jolrEQF8fgf3cSy0T2M+NSTyJiIjKwHsqERHVFjpzJ0tMTMT06dPh4+ODdu3awdfXF0FBQXj27Fm5zxUVFYWJEyeiU6dOcHZ2hpeXFz7//HNcuXJF7TFnz57F+PHj0blzZ7i5uWHo0KHYs2cPBEFQWf7Fixf47bff8P7778Pd3R0eHh6YMGEC/v7773LXl6g8vB2b4OsRXkpPSW0szblsMBERUTnwnkpERLWBnqCuZ6MGuXLlCsaOHYu8vDy4urqiUaNGiImJQUZGBiQSCXbu3Ik6depodK7vv/8ewcHB0NPTg5OTExo1aoSEhAQkJCTA0NAQS5cuxeDBgxWO2bFjB7766isYGRnB09MTRkZG+Pvvv5Gfn4/Bgwfjm2++USgvk8kwffp0hIeHo27duvDw8EBOTg4uXboEQRCwePFiDB8+XGX9evXqBQA4ceJE+RuKqASpTIa4pAxk5haggYUpXJtZ86koEek03kN1U3V4X3lPJSKimqY890/Dyq5MZSsqKsLUqVORl5eH5cuXY8iQIQCAgoICBAYG4uTJk1i5ciUWLlxY5rmio6MRHBwMc3NzhISEoEOHDuK+X3/9FQsWLMD8+fPRqVMnNGrUCACQkJCAJUuWwNLSEr/88gscHBwAAKmpqRg7diwOHDiAHj16YMCAAeK5QkNDER4eDicnJ/z000+oW7cuAODcuXP46KOPsHjxYnh5eaFx48baaiYiJQb6+nBv0bCqq0FERFTj8Z5KRES6rMY/CgkPD8f9+/fh5eUldhoBgKmpKZYtWwZzc3Ps3bsXT548KfNce/fuBQBMnDhRodMIAEaOHIkePXrg+fPniIyMFLeHhIRAJpNhwoQJYqcRADRu3Bjz588HAGzZskXhXMHBwQCAuXPnip1GANC5c2eMHTsWz58/x/bt2zVtAiIiIiIiIiKiSlHjO45OnToFAPD19VXaZ2VlBU9PTxQVFeH06dNlnsvU1BQSiQSenp4q97ds2RIAkJ6eLm6LiopSe/0uXbrA0tISV69eRWZmJgAgPj4eycnJsLa2hru7ckLFfv36KbwuIiIiIiIiIqKqUuM7jm7fvg0AsLe3V7m/TZs2AIBbt26Vea6FCxfi0KFDSqON5OLi4gAAb731FgAgMzMT2dnZMDExQYsWLZTKGxgYiJ1N8uuXVd/WrVtDT08PSUlJeP78eZl1JiIiIiIiIiKqLDU+x1FaWhoAoGFD1fPKra2tASiOEqqIkydPIiYmBkZGRujdu7fCta2traGnp1fq9TMyMhSOsbGxUVnexMQElpaWePz4MbKyspTyHD148OCVXgepVh0Sa+oatqn2sU21j22qfWxTet0YG2kf/461j22qfWxT7WObVg5daNca33GUn58PoHiamSry7Xl5eRW+xq1btzB79mwAxfmP5Imx5dc2MzNTe6yJiQkA4NmzZwr10OQYdXWWSqXih0+VmvyBJCIiehWl3R8fPHggjhomIiIiIs3U+KlqBgYGGpUTBKFC579y5QrGjh2LnJwc+Pj44LPPPhP36ZdjmVX59TWtLwDIZDLNK0pEREREREREpGU1fsTRG2+8gZycHLX5gAoKCgAA5ubm5T53REQEZs2ahfz8fPj6+mLlypUKHT9vvPGGwjVUkddLfv2KHPMyAwMDjioiIiJSobT7Y2mjkYiIiIhItRo/4kieK0ieQ+hl8txG6nIKqbNu3TpMnToV+fn5GDNmDFavXg1jY2OFMvK8SvIV0zS5vvwYdfUtKCjA48ePoa+vL+ZHIiIiIiIiIiKqCjW+40i+Otk///yjcn98fLxCubLIZDLMmjULa9asgb6+PubMmYN58+apnJb25ptvomHDhsjPz0dycrLSfqlUioSEBACARCJRqIe8Xurq26xZMzHXERERERERERFRVajxHUfe3t4AgKNHjyrte/ToEc6fPw8TExN07txZo/PNnTsX+/fvh5mZGdatWwd/f/8KX//MmTN4+vQpnJycxBFHzZo1Q4sWLZCamoqrV68qHRMREQEA8PHx0ai+RERERERERESVRU+oaNboaqKgoAADBgzA/fv3sWjRIowcOVLcPm3aNJw4cQJ+fn6YO3eueExRURHu3bsHAGjatCmMjIwAAAcOHMDMmTNhYGCAzZs3a9TZdPPmTQwdOhRmZmbYsmUL2rVrBwBITU3F2LFjce/ePaxduxa+vr7iMTt37sSiRYvg4OCALVu2oH79+gCAc+fO4aOPPoIgCDh+/Lg4ra0k+YglOzu7ijQXqSFfyper7WgP21T72KbaxzbVvurcpg8ePICBgYHKBzdUczE20r7q/HdcU7FNtY9tqn1s08pRXdu1PHFRje84AoCLFy9i4sSJKCgogJOTE+zs7BAbG4v09HQ4Oztj27ZtYlJqAEhJSRETZJ44cQJ2dnbiEvcPHjxAw4YN4eHhofZ63bp1w6BBg8T/b9q0CStWrIChoSE8PDxgYmKC8+fPIy8vDyNHjsSiRYsUjpfJZJgyZQpOnToFCwsLeHp64unTp4iOjoYgCFixYgUGDhyo8todOnRAYWEh8x8RERGVU0ZGBoyNjREdHV3VVSEtYmxERERUfuWJi3Si4wgAbt++jR9++AEXLlxAXl4e7Ozs0L9/f4wfPx4WFhYKZVV1HF2/fh1DhgzR6Fr+/v6YM2eOwrYTJ07g559/xrVr16Cnp4cWLVpg9OjRGDRokMr8SEVFRdi+fTv27duHpKQkWFhYwNHREZMnT0aHDh0q2ApERERERERERNqjMx1HRERERERERESkXTU+OTYREREREREREVUOw6quAGkmMTER69atw6VLl5CVlYVGjRqhf//+CAgIUMjfVJsdPHgQe/fuxc2bN5Gfn4/69eujc+fOCAgIQMuWLRXKzpw5EwcOHFB7rtGjR2P+/PkK2x4/foxNmzbh2LFjSE1NhaWlJbp27YpPPvkETZo0qYyXVOX+/vtvjB07Vu1+c3NzxMbGKmw7cuQItm3bhoSEBEilUjg4OMDf3x99+/ZVeY6CggJs27YNhw4dQnJyMszMzNCxY0d8/PHHaNu2rVZfT1Xq2bMn7t+/X2Y5Dw8P/PLLLwCANWvWYN26dWrLent7Izg4WGFbbWnPu3fvYvDgwRg+fLjS1GG5s2fPIiQkBDdv3kRBQQFatmyJkSNHYtiwYdDT01Mq/+LFC4SGhuK3337D3bt3YWhoCBcXF0yaNAmdOnVSeQ1d+14oq13z8/Px008/ITIyEklJSZDJZLCzs0Pv3r0xceJEWFpaKh3j4+OD1NRUtddcvXo1+vXrp7CN9zwqCz8jZWNcpH2Mi7SHcZF2MS6qHIyL/sWOoxrgypUrGDt2LPLy8uDq6goXFxfExMRgw4YNOHnyJHbu3Ik6depUdTWrjCAImD59Og4fPgwjIyM4OzujXr16uHnzJvbv34+IiAisX79eYZW8a9euAQDeeecdlTmo5KvjyT169AijR4/GnTt30LRpU3h7eyMxMRH79+/HsWPHsH37dp26+cjJ28nFxQXNmzdX2m9iYqLw/2+//RabN2+Gubk5PD09UVhYiAsXLuDzzz/HlClT8MUXXyiULygowMSJE3Hx4kXY2Nige/fuePDgASIjI3Hy5EmsX78e3bp1q7TX9zr17t0b2dnZKvfJZDJERERAKpXC2dlZ3C5vfx8fH6VcbQDg6Oio8P/a0p6ZmZmYMmUK8vPz1ZbZsWMHvvrqKxgZGcHT0xNGRkb4+++/MXfuXERHR+Obb75RKC+TyfB///d/CA8PR926ddGlSxfk5OTg7NmzOHPmDBYvXozhw4crHKNr3wtltWtOTg78/Pxw+/ZtWFpaws3NTVyJIzg4GEeOHMGOHTsUVgTNzs5Gamoq3nzzTbWfvcaNGyv8n/c8Kgs/I6VjXFR5GBdpD+Mi7WFcVDkYF71EoGqtsLBQ8PHxESQSibBv3z5xe35+vjB58mRBIpEICxYsqLoKVgMHDhwQJBKJ0LVrV+HWrVvi9hcvXgjff/+9IJFIhC5dugjPnj0TBEEQ8vLyhLZt2wpeXl4aX+PLL78UJBKJMGfOHOHFixfi9vXr1wsSiUR49913BalUqr0XVU0EBgYKEolE+PPPP8sse+bMGUEikQg+Pj7C/fv3xe03btwQPD09BYlEIly+fFnhGPn7M3HiRCE/P1/cfuDAAcHe3l7o3Lmz8PTpU+29oGpK3g5jx45V+Hx5eXkJbdu2FfLy8sp1Hl1uz+vXrwt9+vQRJBKJIJFIhCVLliiVuXPnjuDg4CB06NBBuHHjhrj9/v37Qu/evQWJRCKEh4crHLN7925BIpEIQ4YMEXJycsTtZ8+eFVxcXAQXFxeFz7Ug6Nb3gibt+t///leQSCSCn5+fkJ2dLW5//PixMGHCBEEikQgfffSRwjF//vmnIJFIhMDAQI3qwXselYWfkbIxLqo8jIteD8ZFmmNcVDkYFyljx1E1t3//fkEikQjjx49X2pednS28/fbbgpOTk/D48eMqqF31MGbMGEEikQj79+9X2ieTyYR33nlHkEgkwrFjxwRBEISYmBiVf8jq3Lt3T3BwcBA6duwo5ObmKu0fMWKEIJFIhKioqFd6HdWRr6+vIJFIFL4M1fHz8xMkEokQFhamtG/Xrl2CRCIRPv/8c3Fbbm6u4ObmJrRt21ZITU1VOkYenG3fvv3VXkQ1d/r0acHe3l7o1KmTkJmZKW5PS0sTb7Ka0PX2zMnJEb799lvB2dlZkEgkQs+ePdXeyGfNmiVIJBJh/fr1SvvkN+yhQ4cqbO/Vq5cgkUiES5cuKR3z3XffCRKJRPjmm2/EbbryvaBpu+bn5wvt2rUTJBKJUqAoCIKQmZkp2NvbCw4ODgoB5oYNGwSJRCJs3rxZo/rwnkdl4WekbIyLKg/josrHuEgzjIsqB+Mi9Zgcu5o7deoUAMDX11dpn5WVFTw9PVFUVITTp0+/7qpVG5aWlmjVqhXat2+vtE9PTw8tWrQAAKSnpwP4d5hryaGvpfnzzz8hk8nQtWtXlXNI5XNQT548WaH6V1e5ublISkqCra0trKysyiwbHR0NIyMj9OzZU2m/r68v9PT0xLYEgOjoaDx79gwuLi546623lI6Rt6v8b0AXFRYWYtGiRRAEAfPmzUP9+vXFfeX9nOp6e27btg2bNm1CvXr1sH79egwePFht2aioKACqvze7dOkCS0tLXL16FZmZmQCA+Ph4JCcnw9raGu7u7krHqGo7Xfle0LRds7Ky4OTkBHd3d6Uh1ABQv3591K1bFzKZTGxXoPyfY97zqCz8jJSNcVHlYFxU+RgXaY5xUeVgXKQeO46qudu3bwMA7O3tVe5v06YNAODWrVuvrU7Vzbp163DkyBGVCdekUqn4Byq/acj/b2BggC+//BI+Pj5o164d3nnnHQQHB+P58+cK5yjrPWjdujUA3XsPbty4AUEQ0KxZM/z4448YOHAgXF1d4eXlhRkzZiAxMVEse+fOHUilUtja2qq8WdSrVw8NGjRAXl4e7t27B+Df9qpt7VrS5s2bkZSUhC5dumDAgAEK++SfU0tLS8ybNw99+vSBi4sL+vTpg++++w5Pnz5VKK/r7dmoUSPMnDkTkZGRKoNwuczMTGRnZ8PExET8cVSSgYGBmBRW3haa/I3r6ekhKSlJ/H7Qle8FTdvV1tYWO3fuxK5du1TuT0pKQk5ODgwMDGBjYyNuv3btGvT09PDw4UOMGzcOnTp1gpubG0aOHIkjR44onYf3PCoLPyNlY1xUORgXVT7GRZpjXFQ5GBepx46jai4tLQ0AFJJqlWRtbQ3g36dGpGjnzp24f/8+rKysxOz/8hvPqlWrEBMTAycnJzg6OuLevXv4/vvv4e/vj7y8PPEcmr4HGRkZlflSXjt5O509exYbNmyAjY0NPD09AQBhYWF4//33cf78eQBltxGg3E7yz2zJL9OS5NtL9tLrkqdPn2LTpk0AgGnTpintl7f/zz//jOPHj6NNmzZ4++23kZmZiZCQEAwbNkzh717X23P48OH48MMPYWpqWmo5+WfR2tpa5Qoh8n3Av59F+THq2s7ExASWlpaQSqXIyspSOKamfy9o2q5l+e677wAAXl5eYoLGnJwcpKSkQBAEzJgxAzk5OejYsSOaNGmC2NhYBAYGYvHixQrn4T2PysLPyKthXFRxjIsqF+Oi8mFcVDkYF6nHVdWqOXkWd3UfXvn2kjd0Knbu3Dl8++23AIAvv/wSZmZmeP78Oe7cuQMACAwMREBAgLh6SEJCAj777DNcvnwZy5cvx1dffQWg9r4H8hu0u7s71qxZI34xFRYWYvny5dixYwemTp2KY8eOia/dzMxM7fnkK43Iy5Z1jLy8TCZDfn5+qeeuiXbt2oXc3Fx4e3vDxcVFaf/169cBAKNGjcJ///tfGBsbAyi+gUybNg3R0dGYPXs2Nm/eDIDtKSf/e9Xks/js2TMAZbddyWPkZWvr94IqGzZswNGjR2Fqaorp06eL22/cuAGgeHnqVatWoUePHuK+P/74A4GBgdi+fTvc3Nzw7rvvAmC7Utn4Gak4xkWvhnFR5WJcVDkYF71+uhoXccRRNWdgYKBROUEQKrkmNcupU6cwefJkFBYW4oMPPhCXizQxMcG5c+cQHh6OyZMnKyw527JlS3EpytDQUOTm5gLQ/D2Qz1HXFUuXLkVERARCQkLE4AgAjI2NMWfOHLRt2xbZ2dkICwvTuI2Af9upIsfoCqlUil9++QUAMGXKFJVlwsPDERYWhgULFojBEVD8xOG7776DmZkZTp8+LQb8tbk9S1K1jLQ68u/Nyvz86nJbA8Dq1asRFBQEfX19LFu2TGEodadOnfDnn38iLCxMITgCgB49euCzzz4DAGzdulXcznselYWfkYphXPTqGBdVHsZFlYdx0euly3ERO46qOfm86Jfnl8sVFBQAKO65pGK//PILPvnkExQUFMDPzw/z589X2G9paSnOs32Zs7MzGjVqhBcvXoi9wvL3QN7WL5NvVzWHvSYzNjZGixYtYGFhobTPwMAA3t7eAICrV6+W2UbAv59h+We1rGPk5fX19XXuKdCFCxeQnp6O1q1bw9XVVWUZCwsL2NvbqxxW/NZbb8HR0RFAcfsDtbs9S6qMz2JFjtHV7wW5wsJC/N///R9+/PFHGBkZYcWKFXjnnXcUyujp6aFhw4Yq86wAQK9evQAUP8WXB5K851FZ+BkpP8ZF2sG4qPIwLqo8jItej9oQF7HjqJqTzy1VNx+0rPm7tcmLFy8wf/58LFmyBDKZDF9++SXmzp2rdj6vOvJkkfIhf/I5pXwPFMnbKT8/v8w2ApTbqaxj5HN669evX66nJTVBREQEAOC9996r8DnK+znV5fYsSd4OpeUsKO9nsaCgAI8fP4a+vr74lLk2fy9kZWXB398fBw8ehIWFBYKDg8Uh1eXRqFEjAMVPmgsLCwHwnkdl42dEc4yLXi/GRRXHuKjyMC6qfLUlLtLdvxIdIR/e9s8//6jcHx8fr1CutiooKMBHH32E3377Daampli1ahUCAgKUykVHR2PmzJlYuXKl2nM9ePAAwL83IHnbytv6ZfLtEonklV5DdVJYWIj58+fjk08+EZPevaxkO7Vu3RqGhoZITk5W2SOenZ2NrKwsmJmZoWnTpgA0b1dd/Gz/8ccfAKC0YohcfHw8Zs+ejTlz5qg9R0U/p7rYniW9+eabaNiwIfLz85GcnKy0XyqVIiEhAcC/f7Oatl2zZs3EOf218XsBAO7du4fhw4cjNjYWtra2+PXXX+Hl5aWy7J49ezBt2jSEhYWp3P/w4UMAxe+ZfI4+73lUFn5GNMO4SLsYF1UuxkWVh3FR5apNcRE7jqo5+bDXo0ePKu179OgRzp8/DxMTE3Tu3Pk116z6kEql+OSTT3D69GnUq1cPv/zyC/r166eyrEwmw4EDB/DLL78oLdsJAH///TcePnyIxo0bi8sbdu/eHfr6+vjzzz9VDr+MjIwEAPj4+GjxVVUtY2NjnD59GsePH8eJEyeU9hcWFopLRnbv3h0mJibo1KkTCgsLcerUKaXykZGREAQB3bt3F+fqtm/fHhYWFrh8+bL41Kck+dMnXWpXoPjJwIMHD2Btba12qKqpqSn27duHvXv34u7du0r77969i8uXL8Pc3BwdO3YEUHvbU5XSvjfPnDmDp0+fwsnJSXw606xZM7Ro0QKpqaniEPeSVLVdbfxeSEtLg7+/P+7fvw8XFxfs2bNH/J5UVz48PBw7d+5Uuf/AgQMAoDDPn/c8Kgs/I2VjXKR9jIsqD+Oiyse4qHLUtriIHUfVXO/evWFra4uoqCj8+uuv4vaCggLMmTMHeXl5GDFiBOrVq1eFtaxa69evx+nTp2Fubo5t27ahXbt2ast26NABEokE+fn5mD17tkIG+n/++QezZs0CAHz66afiUO6GDRuib9++yM7OxoIFC1BUVCQeExwcjMuXL8Pe3l78w9YVH3zwAQBg5cqVuHnzpri9oKAA//3vf5GUlAQPDw/xi8rf3x8AsHz5ciQlJYnlb968idWrVwOAwtNOExMTjBw5EkVFRZg9e7a4kgNQvKxtREQE6tevj2HDhlXei6wCV65cAQCVK4bI2dnZiTeNWbNmITs7W9z38OFDfP7555BKpRg/fryYa6G2tqcqH3zwAQwNDbF+/XqxvQEgNTVVXOZ08uTJCsfIP79z585VeJp87tw5bNu2DcbGxhg3bpy4vTZ+L8yYMQMPHjyARCLB1q1bUb9+/VLLv//++zA1NUVsbCw2btyokLgxMjISGzduhLGxMT766CNxO+95VBZ+RsrGuKhyMC6qHIyLKh/jospR2+IiPYHLTlR7Fy9exMSJE1FQUAAnJyfY2dkhNjYW6enpcHZ2xrZt23Q20VhZHj9+DG9vb+Tl5aF58+al3nQGDRqEbt26IT4+Hv7+/sjKykL9+vXh6uqK/Px8REdHo6ioCP7+/kpDYTMyMjBq1CgkJyfD1tYWzs7OSExMxO3bt/Hmm29ix44dahNL1lQvXrzAF198gePHj8PQ0BBubm6wsrJCTEwMMjMz0bJlS2zbtk1hZZFFixZh586d4pM2qVSK8+fPo6ioCF9++aXSMPn8/Hz4+fnh6tWrqF+/Pjp06ICHDx8iLi4OJiYmCAkJgaen5+t+6ZXq559/xtdff43//Oc/4tLGqqSnp8PPzw93795FnTp14ObmBqA4gWRBQQH69u2L77//HoaGhuIxtak9165dix9++EHl3ysAbNq0CStWrIChoSE8PDxgYmKC8+fPIy8vDyNHjsSiRYsUystkMkyZMgWnTp2ChYUFPD098fTpU0RHR0MQBKxYsQIDBw5UOEYXvxfUteuZM2fw4YcfAij+oSmfCqDKtGnT0LhxYwDA4cOHMWvWLBQVFaF58+awt7dHSkoKrl27JiaP7N+/v8LxvOdRWfgZUY9xUeVhXFQ5GBdpB+OiysG46F/sOKohbt++jR9++AEXLlxAXl4e7Ozs0L9/f4We9dro2LFj+PTTTzUqO3v2bLFnPCMjA8HBwYiKisLDhw9hZmYGZ2dnjBkzRsxo/7Ls7Gz8+OOPOHnyJNLT02FjYwNPT09MmTJF7dDamk4QBOzduxd79+7F7du3IZVK0aRJE/Tv3x8ffvihUgZ/QRCwb98+7Nq1C/Hx8TAxMUGbNm0wfvx4te2al5eHkJAQHDlyBKmpqbCyssLbb7+NKVOmwMHB4XW8zNcqKCgIGzZswKRJkzB9+vRSy+bm5mLTpk04evQokpOTYWRkBHt7ewwfPhxDhgxRmeC0trRnWQESAJw4cQI///wzrl27Bj09PbRo0QKjR4/GoEGDVCbCLCoqwvbt27Fv3z4kJSXBwsICjo6OmDx5Mjp06KDyGrr2vaCuXZcsWSIulVyWAwcOoG3btuL/r127hpCQEFy8eBE5OTmwsrKCp6cnAgIC1M7J5z2PysLPiGqMiyoX4yLtY1ykHYyLKgfjon+x44iIiIiIiIiIiFRijiMiIiIiIiIiIlKJHUdERERERERERKQSO46IiIiIiIiIiEgldhwREREREREREZFK7DgiIiIiIiIiIiKV2HFEREREREREREQqseOIiIiIiIiIiIhUYscRERERERERERGpxI4jIiIiIiIiIiJSiR1HRNVMSkoK7O3tYW9vj5kzZ5ZZ/vz582L56mrWrFmwt7fH9OnTq7oqlSo3NxeLFi1Ct27d4OzsDC8vL+zZs6eqq0VERFSjMTaquRgbEekGdhwRVWMHDhzAqVOnqroapKHp06dj586dyMjIQIsWLVCvXj3Y2tpWdbWIiIh0BmOjmoWxEZFuMKzqChBR6ebNm4fw8HDUrVu3qqtCpcjLy0NUVBQAYOHChRg5cmTVVoiIiEhHMTaqGRgbEekOjjgiqsb09PSQkZGBJUuWVHVVqAyPHz+GIAgAAA8PjyquDRERkW5ibFRzMDYi0h3sOCKqxkaPHg0ACAsLw4kTJ6q4NlQaqVQq/tvY2LgKa0JERKS7GBvVHIyNiHQHO46IqjE/Pz906NABALBgwQLk5ORofOy+fftgb2+P7t27q9xfMtFkSkqKuH3t2rWwt7dHUFAQ0tPTsWDBAnTv3h0uLi7o3bs3goKCUFhYCKA4+eSECRPQsWNHuLi4YMiQIThw4ECp9UpMTMQXX3wBT09PuLq6YtCgQQgJCcHz58/VHnPx4kV8/vnn6Nq1K5ydndGlSxdMmTIF586dU1m+Z8+esLe3x82bN7FkyRJ07NgRbm5ueP/99zVuw8TERCxYsAB9+vSBs7Mz2rdvjxEjRuDnn39GQUGBQll7e3v06tVL/H+vXr1gb2+PWbNmaXStFy9e4MCBA5g8eTK6desGFxcXuLm5oW/fvpg/fz4SExPFsoIgiOf/+eef1Z5z7ty5sLe3x4wZMxS25+bmYt26dRg8eDDc3Nzw9ttvY+DAgVizZg2ePHmidB755ygwMBCXLl3CoEGD4OzsjK5duypcPz09HUFBQRg2bBg8PDzg5OQEDw8PjBw5Elu2bFFqM7m0tDQsW7YMffv2Rbt27dCtWzfMnz8f6enpYuLQffv2KR2XmZmJb7/9FgMGDICrqyvc3NwwdOhQbNmyRe1n6c6dO5g9ezZ69uwpvqeDBw9GUFAQsrKy1LYlERFVH4yNijE2Ymz0MsZGVJmY44ioGtPT08OyZcswaNAgZGRkYPHixVi5cuVruXZSUhIGDx6MR48eoXXr1jAwMEBycjI2bNiA5ORkdOzYEYsWLYKZmRmaN2+OlJQUXL9+HTNnzkRBQYHKeez//PMPhg0bhmfPnqFNmzaQSqW4efMmbt68id9//x0//fSTUr6C7777DiEhIQCAunXrQiKRID09HSdOnMCJEycwadIktSuSLFq0CDExMWjdujXy8/NhbGyMN998s8zXHhYWhjlz5qCwsBCmpqaQSCR49uwZ4uLiEBcXh9DQUISEhKBRo0YAAHd3dxQWFuJ///sfAMDZ2RnGxsZo3rx5mdcqKChAQEAAzp8/DwCwtbWFRCJBVlYW7t69i7t37+LQoUPYsWMHHB0doaenhyFDhmDt2rUICwvDuHHjlM75/PlzREREAADef/99cfudO3cwadIk3L9/HwYGBmjSpAlMTU0RHx+PdevW4cCBAwgJCUGrVq2UzpmQkICJEyfCwMAAbdq0wZ07d9C6dWsAwOXLlzFp0iQ8efIEJiYmaNq0KQwNDZGSkoLY2FjExsbixIkT2LZtGwwMDMRzXrt2DRMnTkR2djaMjIwgkUiQk5OD3377DSdOnECTJk1UttmlS5cwZcoU5OTkwMjICM2bN4cgCLh27Rr+97//4eDBg9i0aROsra3FY2JjY/Hhhx8iLy8PlpaWaNOmDZ4/f47bt2/jxo0b2L9/P3777Te89dZbZb5nRERUdRgbMTZibKSMsRFVOoGIqpXk5GRBIpEIEolEuHv3riAIgrB161Zx27FjxxTK//333+K+kkJDQwWJRCJ069atzOskJyeL29esWSNu79+/v5CYmCjuW716tbjPwcFB+Prrr4Xnz58LgiAIeXl5woQJEwSJRCL4+PgoXGvmzJnicX369BFu3bol7rt8+bLg5eUlSCQSYcaMGQrH7dq1S5BIJEKHDh2EgwcPittlMpkQHh4uvP3224JEIhF2796tcJyPj494vfDwcHF7VlaWyrYo6fLly4Kjo6MgkUiEuXPnCk+fPhX3Xb9+XfD19RUkEokwZMgQoaioqMz2LIu8vT09PYW4uDiFfXFxcWLbfPbZZ+L2lJQUwd7eXpBIJEJ8fLzSOY8cOSK+DzKZTBAEQXj27JnQp08fQSKRCB9//LHw8OFDsXx6eroQEBAgSCQSwdfXV8jPzxf3yT9HEolEGDFihPDkyRNBEAQhOztbkMlkwosXL4TevXsLEolEmDJlipCTkyMeW1hYKAQHB4vHnzp1StxXUFAg9OrVS5BIJMKECRMU3ptTp04J7u7u4nGhoaHivocPHwoeHh7i+/P48WNxX1JSkjB8+HBBIpEIH3zwgUKbyLcvXrxY/MwKgiDcu3dPfE/nzZun5l0iIqKqxNjoX4yNGBsxNqKqwKlqRDWAn58fOnbsCKB4WPajR49ey3W//vprhSdDkyZNEp+KtG/fHrNmzRLnrJuZmSEgIAAAcP/+fTx+/FjpfAYGBli/fj0kEom4zdXVFStWrAAAHDp0CPfv3wcAFBYWYu3atQCAZcuW4b333hOP0dPTw4ABA8ShxmvXrsWLFy+UrtehQwcMGDBA/H+9evXKfM1r1qzBixcv0LVrVyxevBgWFhbivrZt22LTpk0wNTXFtWvXEB4eXub5ynL27Fno6+vj008/Rbt27RT2tWvXDqNGjQIA3L59W9xua2uLTp06ASh+AviygwcPAgAGDx4MPT09AMCePXuQlJQEJycnrF27Fg0bNhTLW1tbY/Xq1bC1tcXdu3dVDn8GgKlTp6JOnToAACsrK+jp6eHmzZvIycmBsbExlixZovBU1MjICAEBAeLTsZKvITQ0FMnJyWjcuDHWrl2r8N54e3tj8eLFKuuwefNm5OTkoGfPnli8eDEsLS3FfU2bNsWPP/4ICwsLREdH448//hD33bx5EwAwdOhQhTwLTZo0wcyZM+Hj48PlgYmIahDGRoyNGBsVY2xErwM7johqAPmwbDMzM2RmZqq9cWiTlZUVXF1dFbaZmZmJNzFV+QFsbGzEf+fm5irt79y5s8qhvp07d4adnR1kMhn++usvAMXDZzMzM/HGG28ozJEv6b333oO+vj7S0tJw/fp1pf3t27cv5RUqy8vLE4dF+/v7qyzTpEkT9O7dGwC0kpRz165duHLlitolas3MzABAaR68fJj14cOHFbZnZ2fj9OnT4rBtuePHjwMABgwYoDAkWs7U1BR9+/YFAJw6dUppv76+Ptzc3JS2Ozk54eLFi7h48SKsrKyU9hcWFooBU35+vlJ9Bg8eLL7Gkvr3768QwL18XMlguaQGDRrAy8tL6XU0a9YMQPGPi3PnzqGoqEjc17NnT2zYsAEfffSRynMSEVH1w9iIsRFjI8XjGBtRZWKOI6IaomnTpvjyyy+xZMkShIeHo1+/fvD19a2068nnqL9M/kRC1RMqQ8N/v1KE/7/8akmOjo5qrydPRHnnzh0AxXP+AaCoqEhcQUUVAwMDyGQyJCQkKD2VKjmPWxPJycniTdPZ2VltOWdnZxw+fFghMeOrMDIywuPHj3H58mXcvXsXycnJuHv3Lm7cuIHMzEwAgEwmUzjG19cXX331FVJSUnDp0iUxEAwPD0dRURE8PDwU5sHLn2jt2bNHbVAnv1ZCQoLSPktLS5iamqp9DaampkhISMD169dx7949JCcnIz4+Hrdu3RITMpZ8DfL6ODg4qDyfnp4eHB0dkZaWJm579uyZ+NT1xx9/xLZt21QeKy9T8nXMmDEDH3/8MeLi4jBu3DiYm5ujY8eO6NKlC7y9vTXKuUBERNULYyPVGBsxNnoZYyN6Vew4IqpBxowZg8jISFy8eBELFy4UVxWpDKqedJSkr1/+AYtvvPFGmfvkT4+ePn0KoPipTExMTJnnVrXqRWk3c1VKPgmUDztWRT5E+9mzZ+U6v7prLl26FIcOHVJ40mNkZAQnJye0bdtWfNJYkqmpKQYMGIDffvsNhw4dEoMj+VDskokf5dcBICaVLI287UsyMTFRWz4uLg4LFy5UerJpZWWFHj164Pr16wqr0wAQV3AxNzdXe96SQ+EBxfen5NBudUq+ju7du2Pv3r0ICQlBVFQUnj17hj/++AN//PEHvv76a7Rv3x5fffWVmNSSiIhqBsZG6jE2Ymz0MsZGVFHsOCKqQeTDst977z1kZWXhq6++Eud5q6Pq6RagODT2dcnLy1O7T34jk8/LlgdnTk5OaueVa1vJ4O3p06eoX7++ynLyHAWlBXuamjJlCs6fPw9TU1OMGTMGrq6uaNOmDZo1awYjIyPs3r1bZXAEFM9J/+233/D7779jzpw5SE5OxtWrV2Fubi4OrZYzMzPD06dPsWHDBvj4+LxyveXu3LkDf39/FBQUoHXr1hg6dCgcHBzQqlUrcTj1yJEjlYIjMzMzFBUVqRy2L/dy8FkyYD906JBCPghNtG3bFt9//z2KiooQFxeH8+fP4+zZs4iJicGlS5cwbtw4HD16tNSAjYiIqhfGRpWLsVH5MTYiXcQcR0Q1jHxYNgD8/vvviIyMVFlOPle7sLBQ5f709PTKqWApVA3zBYoDuBs3bgCAeMNr0aIFgOKnQKqSO8qP+/vvv3H37l21r7M85EulAhCXj1VFvk8+N7yiLl++LOYNCA4OxqxZs9C/f3+0bt0aRkZGAICHDx+qPd7V1RWtW7dGTk4OLly4gEOHDgEA+vXrp3SDl7enfJi7Knfv3sXVq1eRnZ2t8WvYunUrCgoK0LJlS+zduxcffvghunTpojAHv+SQajn5+3zr1i215355n6WlJRo0aAAAiI+PL/W4GzduiEGsVCpFUlISLl68CKD4iWWHDh3wySefYMeOHdixYwf09PSQkZGBs2fPavjKiYioumBspHgcYyPGRqqOY2xEr4IdR0Q10JgxY+Dh4QEA2Llzp8oy8mR8jx8/RlZWltL+Y8eOVV4F1Th9+rTKG+Xx48fx8OFDGBsbo2vXrgCAjh07ok6dOnj27Jnap2qHDh3C2LFj0b9//1KDCE2Zm5uLK3KomyOenJyMkydPAlCdBLM8Sj5pUpU3ID8/X1ydRCqVqjyHfNj1sWPHEBERAaD4advL5E/S9u7dq5RMEgBevHiBKVOmYNiwYfjmm280fg3yOfOtWrVSOYT/zJkzSE1NVXoNffr0AVD8Hsrn+Zf0119/iecuydvbGwCwfft2pdwGQPHTUH9/fwwePBhbt24FUBwQ+vr6YuzYscjIyFA6xs3NTXxCquqcRERU/TE2KsbYiLHRyxgbkTaw44ioBpIPyzY3N1c73NrV1RVGRkYQBAHLli0Tb4hFRUXYunUrdu/e/TqrDKD4Zv/xxx8r3PTOnj2LOXPmACherUM+BNrc3Fxcwnbp0qUIDQ1VuHEdP34cCxYsAFC8ykTTpk21UsdPP/0UhoaGOH36NObNm6cwXPjmzZuYNGkSnj9/DgcHBwwePPiVrtWyZUvx3+vWrVOYxx8fH49JkyaJc+7VDZ8fNGgQDA0NERYWhoSEBDRt2lRlfofRo0fD2toaSUlJ+Pjjj8WABShebWTq1Km4c+cOjIyM8OGHH2r8GuRP686cOYPo6Ghx+4sXL3D48GEEBgaK20oGZcOGDcNbb72FlJQUTJs2TZzXDwDR0dGYNWuWyusFBATA3Nwcly5dwowZMxSeAN6/fx8BAQHIyclBnTp1xMShDg4OkEgkkEqlmDZtmkIgXVhYiKCgIOTm5sLc3LxSc2MQEVHlYWzE2EiOsRFjI9I+5jgiqqGaNGmCL7/8Uu3ys3Xr1sWECROwYcMGHD58GH/99Rfs7Oxw//595OTkYNSoUTh58qTKp1yVxcfHB2fOnIGvry9at26N/Px8JCUlASh+yjJ16lSF8pMmTUJycjJ2796N//73v1ixYgXs7OyQlpYmDidv3749li5dqrU6urm5YenSpZg7dy52796NsLAwtGrVCnl5eeJKIRKJBD/88IO4ikpFOTo6on///vj999+xZcsW7Nu3D3Z2dsjJyRGfuHl5eeHMmTN49uwZcnNzlZIiNmjQAN26dROXVy25zGxJdevWxfr16/Hxxx/j7Nmz6NWrF1q3bg09PT0kJiaisLAQhoaG+P7772Fvb6/xa/jwww9x+PBhPHr0CKNHj0bz5s3xxhtvICUlBY8fP4a5uTnc3NwQGxurEJRYWFhg9erVGD9+PI4fP44///wTbdq0wbNnz3D37l3Y2tqiQYMGyMzMVFgit1mzZli1ahUCAwNx+PBhREZGonXr1igqKhKH7pubm2Pjxo0KeRiCgoIwcuRIXLhwAb1794adnR3MzMyQkpKCJ0+ewMDAAF999ZXKFXGIiKhmYGzE2AhgbMTYiCoDRxwR1WCjR48Wh2WrEhgYiO+++w7t27dHUVEREhMT0aJFC6xYsQILFy58fRX9/9zd3fHrr7+iS5cuSElJQXp6OlxdXbFs2TKsWbNGnLsup6enh8WLF2Pz5s3o06cPDA0NcePGDTx79gxvv/025s6di59//rnMVU7Ka/DgwTh48CBGjBiBBg0a4J9//sGjR4/g7u6O+fPnY+/evQrLub6KlStXYvHixXBxcYEgCLh16xYKCwvh4+OD4OBgbNmyBY0bNwYAcRj4y+TDr/X19Ut90ufi4oJDhw7hk08+EZf4TUhIQIMGDTB48GCEhoaWexnjxo0bIywsDKNGjULz5s3x4MEDJCYmokGDBvDz80NYWJgY9J4/f14hCairqyvCwsIwbNgw1K9fH7dv30Z+fj4++OAD7N27VwwEX35/e/TogfDwcIwbNw5NmzZFYmIikpKSYGtriw8++ABhYWFwd3dXOKZ169bYv38/Ro0aBVtbW6SmpiI+Ph6WlpYYOnQoDh48iIEDB5brtRMRUfXD2IixEcDYiLERaZueoG4sJxERURXq1KkTHj16hF27dikFO0RERES1DWMjqioccURERK/d2rVr8c4772Djxo0q91+5cgWPHj2CkZFRuZeWJSIiIqppGBtRdcaOIyIieu0cHR0RHx+P9evXKy3zeuvWLcyYMQMA8N577ynlLiAiIiLSNYyNqDrjVDUiInrtBEHAp59+iuPHjwMAGjVqBGtrazx69EhMftm+fXts3LiRwRERERHpPMZGVJ2x44iIiKqETCbDiRMn8OuvvyIxMRHp6emoW7cuWrZsiYEDB2Lo0KEKq4YQERER6TLGRlRdseOIiIiIiIiIiIhUYo4jIiIiIiIiIiJSiR1HRERERERERESkEjuOiIiIiIiIiIhIJXYcERERERERERGRSuw4IiIiIiIiIiIildhxREREREREREREKrHjiIiIiIiIiIiIVGLHERERERERERERqcSOIyIiIiIiIiIiUun/ATsyGJQEJ1yOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nchunks = 50\n",
    "data_chunks = np.linspace(50, len(data), nchunks, dtype = int)\n",
    "off_diags = []\n",
    "fidelities = []\n",
    "\n",
    "for data_chunk in data_chunks:\n",
    "    Key_field = '0'\n",
    "    data = data_list[0][0][Key_field]['click_array'][:data_chunk]\n",
    "    pops, rho, fidelity = twoqb_tomo(data, target_state)\n",
    "    fidelities.append(fidelity)\n",
    "    off_diags.append(np.sum(np.abs(rho.real-np.diag(np.diag(rho.real)))))\n",
    "\n",
    "plt.figure(figsize = (12,5))\n",
    "    \n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(data_chunks, off_diags,\"o\")\n",
    "plt.xlim(0,None)\n",
    "plt.ylabel(r\"$\\Sigma abs(Re(\\rho)_\\mathrm{off~diag})$\")\n",
    "plt.xlabel(\"Number of averages\")\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(data_chunks, fidelities,\"o\")\n",
    "plt.xlim(0,None)\n",
    "plt.ylabel(\"$F$\")\n",
    "plt.xlabel(\"Number of averages\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ae84db6-6adf-486d-bc75-e983e2322e87",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# AllXY"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3ef2f5c-8f81-4491-a10f-3c10afea70f4",
   "metadata": {},
   "source": [
    "## z rotation for A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd85e933-afb7-484b-9aec-c3a74eb180d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "path='Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\ALLXY'\n",
    "timestamp_list = grab_timestamps_hdf(path, \"20240318145645\",\"20240318145645\")\n",
    "data_list = load_from_directory(path, timestamp_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b76f59f-f323-4ebd-998b-b5f48bf11375",
   "metadata": {},
   "outputs": [],
   "source": [
    "Key_field = '0'\n",
    "data = data_list[0][0][Key_field]['click_array']\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "\n",
    "phis = np.linspace(-0.5,0.5,10)\n",
    "x = phis\n",
    "\n",
    "plt.figure(figsize = (6,5))\n",
    "\n",
    "#plt.plot(x,p3,\"o-\")\n",
    "\n",
    "def sine_fit(x,a,b,f,phi):\n",
    "    return a*np.sin(2*np.pi*f*x-phi)+b\n",
    "\n",
    "for i in range(p3.shape[-1]):\n",
    "    plt.errorbar(x,p3[:,i],yerr=np.sqrt(p3[:,i]*(1-p3[:,i])/data.shape[0]),fmt=\"o\")\n",
    "    guess = [1,0,1,1]\n",
    "    try:\n",
    "        est,std,fine,data_fit = fit_function(guess, sine_fit, x, p3[:,i])\n",
    "        plt.plot(fine,data_fit, color = colors[i], alpha = 0.5)\n",
    "    except:\n",
    "        print(\"fit failed\")\n",
    "plt.ylim(0,1)\n",
    "plt.ylabel(r\"$P_{|\\downarrow\\downarrow\\rangle}$\")\n",
    "plt.xlabel(r\"$\\phi/2\\pi$\")\n",
    "plt.tight_layout()\n",
    "# save_fig_manustyle(path+\"\\\\\"+timestamp_list[0]+'_AllXY_populations.pdf')\n",
    "plt.show()\n",
    "plt.figure(figsize = (6,5))\n",
    "diff_data = p3[:,0]-p3[:,1]\n",
    "\n",
    "plt.errorbar(x,diff_data,yerr=np.sqrt((p3[:,0]*(1-p3[:,0])+p3[:,1]*(1-p3[:,1]))/data.shape[0]),color = colors[3],fmt=\"o\")\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, sine_fit, x, diff_data)\n",
    "    plt.title(f\"phi/2pi = {np.round(est[3]/(2*np.pi),3)}±{np.round(std[3]/(2*np.pi),3)}\")\n",
    "    plt.plot(fine,data_fit, color = colors[3], alpha = 0.5)\n",
    "except:\n",
    "    print(\"fit failed\")\n",
    "        \n",
    "\n",
    "\n",
    "#plt.ylim(0,1)\n",
    "plt.ylabel(r\"$P_{|\\downarrow\\downarrow\\rangle}$\")\n",
    "plt.xlabel(r\"$\\phi/2\\pi$\")\n",
    "plt.tight_layout()\n",
    "# save_fig_manustyle(path+\"\\\\\"+timestamp_list[0]+'_AllXY_population_diff.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1bc9722-8096-48de-bff6-1464ea4af44a",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# RB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "732b4b7b-6870-425a-875d-571619cb1449",
   "metadata": {},
   "outputs": [],
   "source": [
    "path='Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\RB'\n",
    "timestamp_list = grab_timestamps_hdf(path,\"20240319171407\",\"20240319175700\")\n",
    "data_list = load_from_directory(path, timestamp_list)\n",
    "\n",
    "gate_numbers = [2,5,10,20,50,100]\n",
    "\n",
    "ref_vals= []\n",
    "for i in range(len(data_list)):\n",
    "    Key_field = '0'\n",
    "    data = data_list[i][0][Key_field]['click_array']\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    ref_vals.append(p3)\n",
    "ref_vals = np.array(ref_vals)\n",
    "    \n",
    "timestamp_list = grab_timestamps_hdf(path,\"20240319181727\",\"20240319190329\")\n",
    "data_list = load_from_directory(path, timestamp_list)\n",
    "\n",
    "aX_vals= []\n",
    "for i in range(len(data_list)):\n",
    "    Key_field = '0'\n",
    "    data = data_list[i][0][Key_field]['click_array']\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    aX_vals.append(p3)\n",
    "aX_vals = np.array(aX_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a824f9a3-f583-4cc6-bacf-d67699e7ced8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def p_decay(m, p, A, B):\n",
    "    return 0.48*np.power(p, m) + 0.5\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.xlabel('Number of Cliffords')\n",
    "plt.ylabel(f'{labels[3]} population')\n",
    "\n",
    "\n",
    "popt, pcov = sp.curve_fit(p_decay, gate_numbers, ref_vals, [0.9,0.5,0.5])\n",
    "plt.errorbar(gate_numbers,ref_vals,yerr=np.sqrt(ref_vals*(1-ref_vals)/data.shape[0]),fmt=\"ko\",label = f\"ref p ={np.round(popt[0],3)}\")\n",
    "plt.plot(gate_numbers, p_decay(np.array(gate_numbers), *popt),'k')\n",
    "pref=popt[0]\n",
    "popt, pcov = sp.curve_fit(p_decay, gate_numbers, aX_vals, [0.9,0.5,0.5])\n",
    "plt.errorbar(gate_numbers,aX_vals,yerr=np.sqrt(aX_vals*(1-aX_vals)/data.shape[0]),fmt=\"ro\", label = f\"aX p ={np.round(popt[0],3)}\")\n",
    "plt.plot(gate_numbers, p_decay(np.array(gate_numbers), *popt),'r')\n",
    "pgate=popt[0]\n",
    "\n",
    "plt.title(f'gate error={(1-pgate/pref)/2:.4f}')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7afdf45-e748-42f2-b580-8945ea0ce65f",
   "metadata": {},
   "outputs": [],
   "source": [
    "path='Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\RB'\n",
    "timestamp_list = grab_timestamps_hdf(path,\"20240320130232\",\"20240320132902\")\n",
    "data_list = load_from_directory(path, timestamp_list)\n",
    "gate_numbers = [2,5,10,20,50,100]\n",
    "\n",
    "ref_vals= []\n",
    "num_ref_avgs = []\n",
    "for i in range(len(data_list)):\n",
    "    Key_field = '0'\n",
    "    data = data_list[i][0][Key_field]\n",
    "    p3s = []\n",
    "    num_avgs = 0\n",
    "    for batch in range(data['N_batches']):\n",
    "        click_array = data[f'click_array_{batch}']\n",
    "        num_avgs += len(click_array)\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(click_array, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "        p3s.append(p3)\n",
    "    ref_vals.append(p3s)\n",
    "    num_ref_avgs.append(num_avgs)\n",
    "ref_vals = np.array(ref_vals).mean(1)\n",
    "\n",
    "path='Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\iRB'\n",
    "timestamp_list = grab_timestamps_hdf(path,\"20240320135426\",\"20240320142254\")\n",
    "data_list = load_from_directory(path, timestamp_list)\n",
    "\n",
    "ax_vals= []\n",
    "num_ax_avgs = []\n",
    "for i in range(len(data_list)):\n",
    "    Key_field = '0'\n",
    "    data = data_list[i][0][Key_field]\n",
    "    p3s = []\n",
    "    num_avgs = 0\n",
    "    for batch in range(data['N_batches']):\n",
    "        click_array = data[f'click_array_{batch}']\n",
    "        num_avgs += len(click_array)\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(click_array, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "        p3s.append(p3)\n",
    "    ax_vals.append(p3s)\n",
    "    num_ax_avgs.append(num_avgs)\n",
    "ax_vals = np.array(ax_vals).mean(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0f22a07-fb2b-457d-8770-04ba89ee97e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def p_decay(m, p, A, B):\n",
    "    return A*np.power(p, m) + 0.5\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.xlabel('Number of Cliffords')\n",
    "plt.ylabel(f'{labels[3]} population')\n",
    "\n",
    "\n",
    "popt, pcov = sp.curve_fit(p_decay, gate_numbers, ref_vals, [0.9,0.5,0.5])\n",
    "plt.errorbar(gate_numbers,ref_vals,yerr=np.sqrt(ref_vals*(1-ref_vals)/num_ref_avgs[0]),fmt=\"ko\",label = f\"ref p ={np.round(popt[0],3)}\")\n",
    "plt.plot(gate_numbers, p_decay(np.array(gate_numbers), *popt),'k')\n",
    "print(popt)\n",
    "pref=popt[0]\n",
    "popt, pcov = sp.curve_fit(p_decay, gate_numbers, aX_vals, [0.9,0.5,0.5])\n",
    "plt.errorbar(gate_numbers,aX_vals,yerr=np.sqrt(aX_vals*(1-aX_vals)/num_ax_avgs[0]),fmt=\"ro\", label = f\"aX p ={np.round(popt[0],3)}\")\n",
    "plt.plot(gate_numbers, p_decay(np.array(gate_numbers), *popt),'r')\n",
    "print(popt)\n",
    "pgate=popt[0]\n",
    "\n",
    "plt.title(f'gate error={(1-pgate/pref)/2:.4f}')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5e137ba-da7a-4a41-93c2-b5a1fc3ff6b5",
   "metadata": {},
   "source": [
    "## Actually RB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80911264-9ab0-42fd-98a4-5e4c18c360ee",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "path='Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\RB'\n",
    "timestamp_list = grab_timestamps_hdf(path,\"20240324232848\",\"20240324232848\")\n",
    "data_list = load_from_directory(path, timestamp_list)\n",
    "\n",
    "path='Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\iRB'\n",
    "timestamp_list = grab_timestamps_hdf(path,\"20240325012936\",\"20240325033024\")\n",
    "[data_list.append(x) for x in load_from_directory(path, timestamp_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ef1c13c-6a5d-4f14-a559-2a344643097e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "gate_labels = [\"Reference\",\"Pi\",\"Pi/2\"]\n",
    "gate_numbers = [int(i) for i in sinhspace_asymm(1,110, 10,nonlinearity = 3)]\n",
    "gate_pvals = [] \n",
    "curves = []\n",
    "p_decay = lambda x, p, A, B: A*p**x+B\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "for i in range(len(data_list)):\n",
    "    Key_field = '0'\n",
    "    data = data_list[i][0][Key_field]['click_array']\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    popt, pcov ,fine, data_fit = fit_function([1,0.5,0.5], p_decay, gate_numbers, p3, lb=[0.5,0,0.499], ub=[1,1,0.501])\n",
    "    print(pcov)\n",
    "    curves.append([fine,data_fit])\n",
    "    plt.errorbar(gate_numbers,p3,yerr=np.sqrt(p3*(1-p3)/data.shape[0]),fmt=\"o\",color= colors[i],label = f\"{gate_labels[i]} p ={np.round(popt[0],3)}\")\n",
    "    plt.plot(fine, data_fit, color= colors[i], alpha=0.5)\n",
    "    gate_pvals.append(popt[0])\n",
    "    \n",
    "plt.title(f\"Gate Error: $π_A$={(1-gate_pvals[1]/gate_pvals[0])/2:.3f}, $π/2_A$={(1-gate_pvals[2]/gate_pvals[0])/2:.3f}\")\n",
    "plt.xlabel('Number of Cliffords')\n",
    "plt.ylabel(f'{labels[3]} population')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "# print(f\"Pi fidelity={(1-gate_pvals[1]/gate_pvals[0])/2}\")\n",
    "# print(f\"Pi/2 gate fidelity ={(1-gate_pvals[2]/gate_pvals[0])/2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e7e41bd-5d71-4f5e-834c-4417ceb34c00",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "path='Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\RB'\n",
    "timestamp_list = grab_timestamps_hdf(path,\"20240325053114\",\"20240325053114\")\n",
    "data_list = load_from_directory(path, timestamp_list)\n",
    "\n",
    "path='Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\iRB'\n",
    "timestamp_list = grab_timestamps_hdf(path,\"20240325073203\",\"20240325093252\")\n",
    "[data_list.append(x) for x in load_from_directory(path, timestamp_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9c25ade-a474-4f8a-8879-21fc7d08ee11",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def three_p_decay(x,A1,A2,A3,p1,p2,p3,B):\n",
    "    return A1*p1**x + A2*p2**x + A3*p3**x + 3*B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0559a694-4843-4ae6-ba92-054669b86cc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_data = data_list[0][0][Key_field]['click_array']\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "popt, pcov ,fine, data_fit = fit_function([1,0.5,0.5], p_decay, gate_numbers, p3, lb=[0.5,0,0], ub=[1,1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "408d20e8-3296-488b-a1de-5d315f39334d",
   "metadata": {},
   "outputs": [],
   "source": [
    "gate_labels = [\"Reference\",\"Pi\",\"Pi/2\"]\n",
    "gate_numbers = [int(i) for i in sinhspace_asymm(1,110, 10,nonlinearity = 3)]\n",
    "gate_pvals = [] \n",
    "curves = []\n",
    "p_decay = lambda x, p, A,B: A*p**x+B\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "for i in range(len(data_list)):\n",
    "    Key_field = '0'\n",
    "    data = data_list[i][0][Key_field]['click_array']\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    popt, pcov ,fine, data_fit = fit_function([1,0.5,0.5], p_decay, gate_numbers, p3, lb=[0.5,0,0], ub=[1,1,1])\n",
    "    print(pcov)\n",
    "    curves.append([fine,data_fit])\n",
    "    plt.errorbar(gate_numbers,p3,yerr=np.sqrt(p3*(1-p3)/data.shape[0]),fmt=\"o\",color= colors[i],label = f\"{gate_labels[i]} p ={np.round(popt[0],3)}\")\n",
    "    plt.plot(fine, data_fit, color= colors[i], alpha=0.5)\n",
    "    gate_pvals.append(popt[0])\n",
    "    \n",
    "plt.title(f\"Gate Error: $π_B$={(1-gate_pvals[1]/gate_pvals[0])/2:.3f}, $π/2_B$={(1-gate_pvals[2]/gate_pvals[0])/2:.3f}\")\n",
    "plt.xlabel('Number of Cliffords')\n",
    "plt.ylabel(f'{labels[3]} population')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "# print(f\"Pi fidelity={(1-gate_pvals[1]/gate_pvals[0])/2}\")\n",
    "# print(f\"Pi/2 gate fidelity ={(1-gate_pvals[2]/gate_pvals[0])/2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56cd9f73-35ce-492a-b83c-cdc130ac37f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(curves[0][0],(curves[0][1]-curves[2][1])/np.sqrt(curves[2][1]*(1-curves[2][1])/data.shape[0]))#*curves[0][0])\n",
    "plt.plot(curves[0][0],(curves[0][1]-curves[1][1])/np.sqrt(curves[1][1]*(1-curves[1][1])/data.shape[0]))#*curves[0][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01c5729a-e171-4d7a-83e2-2ff5fdca0e41",
   "metadata": {},
   "source": [
    "# IRB Sweeping pulse params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fef5c26-9b25-414b-94ed-b4267efa9a91",
   "metadata": {},
   "outputs": [],
   "source": [
    "path='Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\iRB'\n",
    "timestamp_list = ['20240324014814','20240324054928']#grab_timestamps_hdf(path,\"20240321014039\",\"20240321044153\")\n",
    "data_list = load_from_directory(path, timestamp_list)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b651d215-8123-4939-9598-c08738ac6047",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(timestamp_list)):\n",
    "    Key_field = '0'\n",
    "    data = data_list[i][0][Key_field]['click_array']\n",
    "    x = data_list[i][0][Key_field]['x']\n",
    "    filename = timestamp_list[i]+'_iRB'\n",
    "    \n",
    "    print('Data shape:', data.shape)\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    print(p3)\n",
    "    \n",
    "    \n",
    "    plt.figure(figsize=(7,7))\n",
    "    \n",
    "    quadratic_function = lambda x,*y: y[0]+y[1]*x+y[2]*x**2\n",
    "    guess = [0.65, 0, -1]\n",
    "    est,std,fine, data_fit = fit_function(guess, quadratic_function,x,p3 )\n",
    "    plt.errorbar(x,p3,yerr=np.sqrt(p3*(1-p3)/data.shape[0]),fmt=\"ko\",label = 'data')\n",
    "    plt.plot(fine,data_fit, label = 'quadratic fit')\n",
    "    maximum = -est[1]/(2*est[2])\n",
    "    plt.annotate(r\"Max $F$ at $\\Delta_{\\tau} = %.3f$ ms\"%maximum, xy=(0.05, 0.95), xycoords='axes fraction')\n",
    "    \n",
    "    \n",
    "    plt.xlabel(r'$\\Delta_{\\tau}$ (ms)')\n",
    "    plt.ylabel(f'{labels[3]} population')\n",
    "    #plt.ylim([0.50, None])\n",
    "    plt.legend()\n",
    "    \n",
    "    save_fig_manustyle(path+'\\\\'+filename+'_'+'population.pdf')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09778b6c-3d3f-4878-a645-91d2dbf7ccfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bfbe35b-d78f-4a36-94c1-d9978f831f09",
   "metadata": {},
   "source": [
    "# CZ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3bb2e51-38fc-490d-8241-3f9fc9095f45",
   "metadata": {},
   "outputs": [],
   "source": [
    "path='Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\CZ'\n",
    "timestamp_list = grab_timestamps_hdf(path,\"20240321014039\",\"20240321044153\")\n",
    "data_list = load_from_directory(path, timestamp_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95dc9cc0-0b9c-43e4-a766-6b51ff399235",
   "metadata": {},
   "outputs": [],
   "source": [
    "CZ_durations = [int(CZ_duration//4) for CZ_duration in np.linspace(100,4e6,21)]\n",
    "def ramsey_fit(t,f,phi,A,B):\n",
    "    return A*np.cos(2*np.pi*f*t+phi)+B\n",
    "sigmoid_length = 5\n",
    "factors = [1, 2, 5, 15]\n",
    "\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ceaf1304-db56-44c1-899a-179d878ac5cc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for l in range(len(data_list)):\n",
    "    Key_field = '0'\n",
    "    full_data = data_list[l][0][Key_field]['click_array']\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(full_data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    for k in range(2):\n",
    "        ###########################################################\n",
    "        data = full_data[:,:,k]\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        ############################### Plotting ###############################\n",
    "        fig,ax=plt.subplots(2,1,figsize=(10,12))\n",
    "        x = 4e-6*np.array(CZ_durations)*factors[l]**2\n",
    "        ############################### subplot 0 ###############################\n",
    "        for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "        ax[0].set_ylabel(\"Mean counts\")\n",
    "        ax[0].set_xlabel('CZ pulse time (ms)')\n",
    "        ax[0].legend(loc = \"lower right\")\n",
    "\n",
    "        ############################### subplot 1 ###############################    \n",
    "        fit_results = []\n",
    "        if len(readout_freqs)==4:\n",
    "            for i in range(len(readout_freqs)): \n",
    "                guess = [0.4/factors[l]**2, (i)//3*np.pi/2,0.45,0.5]\n",
    "                try:\n",
    "                    est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, pops[i])\n",
    "                    ax[1].plot(fine,data_fit, color = colors[i])\n",
    "                    # ax[1].plot(fine,ramsey_fit(fine,*guess), color = colors[i],alpha = 0.5)\n",
    "                except:\n",
    "                    print(\"fit failed\")\n",
    "                    est = guess\n",
    "                    std = guess\n",
    "                fit_results.append(est)\n",
    "                ax[1].plot(x,pops[i],\"o\", label = labels[i])\n",
    "        else:\n",
    "            ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "\n",
    "        if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "        ax[1].set_ylabel(\"P\")\n",
    "        ax[1].set_xlabel('CZ duration (ms)')\n",
    "        ax[1].legend(loc = \"lower right\")\n",
    "        # plt_label = f'f = {est[0]:.4f}$\\pm${std[0]:.4f} kHz, $\\delta $f = {(est[0] - ramsey_detuning*1e6):.4f} kHz \\n T$_1$ = {est[1]:.2f}$\\pm${std[1]:.4f} ms'\n",
    "\n",
    "        plt_label = \"Fitted oscillation rate: f1 = %.1f Hz,  f2 = %.1f Hz\"%(fit_results[2][0]*1e3,fit_results[3][0]*1e3)\n",
    "        ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "        ax[1].set_ylim(0,1)\n",
    "        plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d65ae670-1192-4090-a9d5-4bf08ac73e17",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_data = data_list[1][0][Key_field]['click_array']\n",
    "val = []\n",
    "plot_labels = [\"Spin A down\",\"Spin A up\"]\n",
    "for k in range(2):\n",
    "    ###########################################################\n",
    "    data = full_data[:,:,k]\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "    pop = p3-p2\n",
    "    plt.plot(4e-6*np.array(CZ_durations)*factors[1]**2,pop,\"o-\",color = colors[k],label = plot_labels[k])\n",
    "    # plt.plot(4e-6*np.array(CZ_durations)*factors[1]**2,p3,\"o-\",color = colors[k+2])\n",
    "plt.legend()\n",
    "plt.xlabel(\"CZ duration ms\")\n",
    "plt.ylabel(\"Phase of spin B (a.u.)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e37ec0d-7389-45de-94bc-0b809dbc4d54",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(4,1,figsize=(10,12))\n",
    "CZ_phases = []\n",
    "for i in range(len(factors)):\n",
    "    full_data = data_list[i][0][Key_field]['click_array']\n",
    "    val = []\n",
    "    plot_labels = [\"Spin A down\",\"Spin A up\"]\n",
    "    diff= []\n",
    "    for k in range(2):\n",
    "        ###########################################################\n",
    "        data = full_data[:,:,k]\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "        pop = p3-p2\n",
    "        diff.append(pop)\n",
    "        ax[i].plot(4e-6*np.array(CZ_durations)*factors[i]**2,pop,\"o-\",color = colors[k],label = plot_labels[k])\n",
    "    # ax[i].plot(4e-6*np.array(CZ_durations)*factors[i]**2,(diff[1]-diff[0])/2,\"--\",color = colors[2],label = \"Difference\")\n",
    "    CZ_phases.append((diff[0]-diff[1])/2)\n",
    "        # plt.plot(4e-6*np.array(CZ_durations)*factors[1]**2,p3,\"o-\",color = colors[k+2])\n",
    "    ax[i].legend()  \n",
    "    ax[i].set_ylim(-1,1)\n",
    "    ax[i].set_title(f\"Pulse amplitude = 0.02/{factors[i]}\")\n",
    "    ax[i].set_ylabel(\"Phase of spin B (a.u.)\")\n",
    "ax[3].set_xlabel(\"CZ duration ms\")\n",
    "plt.tight_layout()\n",
    "save_fig_manustyle(path+\"\\\\\"+timestamp_list[0]+'_amplitude_variation.pdf')\n",
    "plt.show()\n",
    "print(path+\"\\\\\"+timestamp_list[0]+\"_amplitude_variation.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77341f35-9103-4280-bd95-b1b91adc3318",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(4,1,figsize=(10,12))\n",
    "for i in range(len(factors)):\n",
    "    x = 4e-6*np.array(CZ_durations)*factors[i]**2           \n",
    "    guess = [0.4/factors[i]**2, (i)//3*np.pi/2,0.45,0.5]\n",
    "    try:\n",
    "        est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, CZ_phases[i])\n",
    "        ax[i].plot(fine,data_fit, color = \"k\")\n",
    "        ax[i].plot(fine,ramsey_fit(fine,*guess), color = colors[i],alpha = 0.5)\n",
    "    except:\n",
    "        print(\"fit failed\")\n",
    "        est = guess\n",
    "        std = guess\n",
    "    fit_results.append(est)\n",
    "    ax[i].plot(x, CZ_phases[i],\"o\", label = labels[i])\n",
    "    ax[i].set_ylim(-1,1)\n",
    "ax[3].set_xlabel(\"CZ duration ms\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92155bfe-7ca6-4f1f-b90e-b292d1294ac5",
   "metadata": {},
   "source": [
    "# Frame corrections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26754934-ca6b-4048-beab-3e0e8dd2772e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_list[l][0][Key_field].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f67e01ca-99a7-471f-897c-4b58a09e29d7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "path='Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\CZ'\n",
    "timestamp_list = grab_timestamps_hdf(path,\"20240327170531\",\"20240327170531\")\n",
    "data_list = load_from_directory(path, timestamp_list)\n",
    "\n",
    "def ramsey_fit(t,f,phi,A,B):\n",
    "    return A*np.cos(2*np.pi*f*t+phi)+B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a02768d5-5289-42d1-aa2e-cca2cb4024ea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9c8dce2-6047-41a9-b4a9-19463dc98cf8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "phase_rotation = [float(phase) for phase in np.linspace(-0.5,0.5,11)]\n",
    "plot_guess=False\n",
    "for l in range(len(data_list)):\n",
    "    Key_field = '0'\n",
    "    full_data = data_list[l][0][Key_field]['click_array']\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(full_data, frequency_domain = True, accumulated=True)\n",
    "    pops = [p0+p1,p2+p3]\n",
    "    \n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "    fig,ax=plt.subplots(1,1,figsize=(7,4),tight_layout=True)\n",
    "    x = phase_rotation\n",
    "    ############################### subplot 0 ###############################\n",
    "\n",
    "    ############################### subplot 1 ###############################    \n",
    "    fit_results = []\n",
    "    for i in range(2): \n",
    "        guess = [1, 0.5, -0.5,0.5]\n",
    "        try:\n",
    "            est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, pops[i])\n",
    "            ax.plot(fine,data_fit, color = colors[i])\n",
    "            #ax.plot(fine,ramsey_fit(fine,*guess), color = colors[i],alpha = 0.5)\n",
    "\n",
    "        except Exception as e:\n",
    "            print(\"fit failed, \", e)\n",
    "            est = guess\n",
    "            std = guess\n",
    "            \n",
    "        fit_results.append(est)\n",
    "        ax.plot(x,pops[i],\"o\")\n",
    "\n",
    "    if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "    ax.set_ylabel(\"P\")\n",
    "    ax.set_xlabel(f'Artificial detuning/2$\\pi$')\n",
    "    plt_label = f\"z_phase_rotation_b_half_on_a_prep: $\\Delta \\phi += ${(-fit_results[1][1])/(2*np.pi):.3f} $\\cdot 2\\pi$\"\n",
    "    ax.set_title(plt_label, fontsize = \"small\")\n",
    "    ax.set_ylim(0,1)\n",
    "    print(fit_results[3])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db809a8e-1d0c-4291-9ef9-51283cbb8b44",
   "metadata": {},
   "outputs": [],
   "source": [
    "path+timestamp_list[0]+'.pdf'"
   ]
  }
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